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APPLICATION OF KNOWLEDGE-BASED FUZZY INFERENCE SYSTEM

ON HIGH VOLTAGE TRANSMISSION LINE MAINTENANCE

A Thesis Submitted for the Degree of

Master of Engineering

By

Mohd Junaizee Mohd Noor, B.Sc. Elect. Eng. (Missouri)

School of Electrical & Electronic Systems Engineering

Queensland University of Technology

2004

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KEYWORDS

High voltage transmission lines; insulators; tower structures; foundations; conductors;

maintenance management; visual inspection; artificial intelligence; fuzzy logic; fuzzy

inference systems; knowledge-based systems

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ABSTRACT

A majority of utilities conduct maintenance of transmission line components based on the

results of routine visual inspection. The inspection is normally done by inspectors who

detect defects by visually checking transmission line components either from the air (in

helicopters), from the ground (by using high-powered binoculars) or from the top of the

structure (by climbing the structure).

The main problems with visual inspection of transmission lines are that the determination

of the defects varies depending on the inspectors’ knowledge and experience and that the

defects are often reported qualitatively using vague and linguistic terms such as “medium

crack”, “heavy rust”, “small deflection”. As a result of these drawbacks, there is a large

variance and inconsistency in defect reporting (which, in time, makes it difficult for the

utility to monitor the condition of the components) leading to ineffective or wrong

maintenance decisions. The use of inspection guides has not been able to fully address

these uncertainties.

This thesis reports on the application of a visual inspection methodology that is aimed at

addressing the above-mentioned problems. A knowledge-based Fuzzy Inference System

(FIS) is designed using Matlab’s Fuzzy Logic Toolbox as part of the methodology and its

application is demonstrated on utility visual inspection practice of porcelain cap and pin

insulators. The FIS consists of expert-specified input membership functions (representing

various insulator defect levels), output membership functions (indicating the overall

conditions of the insulator) and IF-THEN rules. Consistency in the inspection results is

achieved because the condition of the insulator is inferred using the same knowledge-base

in the FIS rather than by individual inspectors. The output of the FIS is also used in a

mathematical model that is developed to suggest appropriate component replacement

date.

It is hoped that the methodology that is introduced in this research will help utilities

achieve better maintenance management of transmission line assets.

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CONTENTS

Keywords ....................................................................................................................................................... i Abstract ......................................................................................................................................................... ii Contents ....................................................................................................................................................... iii List of Figures ............................................................................................................................................. vi List of Tables ............................................................................................................................................... ix List of Abbreviations .................................................................................................................................. x Statement of Original Authorship .......................................................................................................... xii Acknowledgments .................................................................................................................................... xiii Chapter 1: Introduction ....................................................................................................... 1

1.1 Justification for and Introduction to the Research ......................................................................... 1 1.2 Aims and Objectives of the Research ............................................................................................... 4 1.2 Organization of the Thesis .................................................................................................................. 4 Chapter 2: Components of Transmission Lines and their Failure Modes ...................... 7

2.1 Chapter Overview ................................................................................................................................. 7 2.2 Towers and Structures ......................................................................................................................... 7 2.2.1 Functions of Transmission Towers ....................................................................................................8 2.2.2 Failure Modes of Steel Transmission Towers ................................................................................ 12 2.3 Foundations ......................................................................................................................................... 13 2.3.1 Functions of Foundations .................................................................................................................. 13 2.3.2 Failure Modes of Foundations .......................................................................................................... 16 2.4 Conductors and Earth Wires ............................................................................................................ 18 2.4.1 Function of Conductors and Earth Wires ...................................................................................... 18 2.4.2 Failure Modes of Conductors............................................................................................................ 21 2.4.2.1 Conductor Corrosion .......................................................................................................... 22 2.4.2.2 Conductor Vibration ........................................................................................................... 24

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2.4.2.3 Conductor Annealing .......................................................................................................... 27 2.5 Insulators .............................................................................................................................................. 27 2.5.1 Functions of Insulators ....................................................................................................................... 27 2.5.2 Failure Modes of Insulators ............................................................................................................... 31 2.5.2.1 Mechanical Failures .............................................................................................................. 31 2.5.2.2 Electrical Failures ................................................................................................................. 34 2.5.2.3 Audible Noise and Radio Interference ............................................................................ 38 2.6 Chapter Summary ............................................................................................................................... 39

Chapter 3: Inspection, Diagnosis and Maintenance of Transmission Line Components ...................................................................................................................... 41 3.1 Chapter Overview ............................................................................................................................... 41 3.2 Review of Component Diagnosis Methods .................................................................................. 41 3.2.1 Test for Tower Structural Strength .................................................................................................. 42 3.2.2 Diagnostic Tests on Tower Foundations ....................................................................................... 43 3.2.3 Diagnostic Tests on Conductors ...................................................................................................... 45 3.2.4 Insulator Diagnostic Tests ................................................................................................................. 46 3.3 Review of Inspection and Maintenance Methods ........................................................................ 53 3.3.1 McMahon Survey of Inspection Practice of Australian and New Zealand Utilities .............. 54 3.3.2 CIGRE Survey of Utility Assessment of Existing Transmission Lines ................................... 55 3.4 Visual Inspection ................................................................................................................................ 56 3.4.1 Ground-level and Climbing Inspection ........................................................................................... 56 3.4.2 Aerial Inspection .................................................................................................................................. 58 3.4.3 Drawbacks of Visual Inspection ....................................................................................................... 60 3.5 Chapter Summary ............................................................................................................................... 61 Chapter 4: Fuzzy Logic and Fuzzy Inference System ..................................................... 63

4.1 Chapter Overview ............................................................................................................................... 63 4.2 Fuzzy Logic .......................................................................................................................................... 63 4.2.1 Membership Functions ....................................................................................................................... 64 4.2.2 Fuzzy IF-THEN Rules ....................................................................................................................... 67 4.3 Fuzzy Inference System ..................................................................................................................... 68 4.3.1 Inference and Defuzzification Techniques ..................................................................................... 69

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4.3.2 Developing Fuzzy Inference Systems .............................................................................................. 72 4.4 Applications of Fuzzy Logic in Utility Environment .................................................................. 73 4.5 Chapter Summary ............................................................................................................................... 75 Chapter 5: Using Fuzzy Logic for Transmission Line Defect Assessment .................... 77

5.1 Chapter Overview ............................................................................................................................... 77 5.2 Application of the FIS on Porcelain Cap and Pin Insulators ..................................................... 78 5.2.1 Factors Affecting Pin Corrosion....................................................................................................... 79 5.2.2 Factors Affecting Chipped/Broken Porcelain Insulator Disc.................................................... 83 5.2.3 The Insulator Visual Inspection Process ........................................................................................ 89 5.2.4 The Fuzzy Inference System for Visual Inspection of Insulators ............................................. 91 5.2.4.1 Assessment of Single Insulators ........................................................................................ 91 5.2.4.2 Assessment of Multiple Insulators in a String .............................................................. 112 5.2.4.3 Assessment of Multiple Insulator Strings in a Transmission Line ........................... 114 5.3 Application of the FIS on Other Transmission Line Components ....................................... 118 5.4 Potential Savings due to Introduction of FIS in Tenaga Nasional Berhad’s Transmission Line Inspection and Maintenance Practice .............................................................. 120 5.5 Chapter Summary ............................................................................................................................ 122 Chapter 6: Conclusions and Future Work ...................................................................... 124

6.1 Summary of the Research .............................................................................................................. 124 6.2 Major Research Contributions ...................................................................................................... 129 6.3 Future Work ..................................................................................................................................... 130 References ....................................................................................................................... 131 Appendix.......................................................................................................................... 138

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LIST OF FIGURES

Figure 2-1: Example of a typical overhead transmission line.............................................................. 8

Figure 2-2: Monopole and steel lattice tower designs for 220 kV transmission lines .................. 11

Figure 2-3: Structural failures of a transmission line due to an ice storm in Canada .................... 12

Figure 2-4: Uplift and compression forces on tower foundations ................................................... 14

Figure 2-5: Typical construction of transmission tower steel grillage foundation ........................ 15

Figure 2-6: Typical construction of transmission tower rock anchor foundation ........................ 16

Figure 2-7: Typical ACSR conductor stranding arrangements ......................................................... 18

Figure 2-8: Arrow showing evidence of rust on steel core of ACSR conductor .......................... 22

Figure 2-9: Sectional view of ACSR conductor illustrating galvanic corrosion mechanism ....... 23

Figure 2-10: Arrow showing signs of corrosion at a conductor mid span joint ............................ 24

Figure 2-11: Multiple fatigue cracks of outermost aluminum strands of an ACSR

conductor .................................................................................................................................................... 25

Figure 2-12: Cross-sectional view of a cap and pin insulator ............................................................ 29

Figure 2-13: Porcelain cap and pin insulator string ............................................................................. 29

Figure 2-14: Typical polymeric insulator construction ....................................................................... 30

Figure 2-15: Pin corrosion is most severe at cement interface ......................................................... 32

Figure 2-16: Brittle fractures on composite insulators ....................................................................... 33

Figure 2-17: ‘Doughnut’-like separation of the porcelain shell from its cap and pin ................... 36

Figure 3-1: Sequence photograph of a transmission tower collapse during a structural in-

situ test ......................................................................................................................................................... 43

Figure 3-2: Diagram of the half-cell measurement method .............................................................. 44

Figure 3-3: Electric field distribution across an insulator string with a punctured unit in the

middle .......................................................................................................................................................... 49

Figure 3-4: Variation of electric field along an 18-unit insulator string which shows

defective insulator units at insulator number 7, 11, 14 and 15 ......................................................... 50

Figure 3-5: Aerial visual inspection of transmission line components using the helicopter ....... 59

Figure 3-6: Broken conductor strands due to gunshot ...................................................................... 59

Figure 4-1: Triangular membership function (10, 20, 30) .................................................................. 65

Figure 4-2: Intersection of 2 membership functions (A AND B) ................................................... 66

Figure 4-3: Union of 2 membership functions (A OR B) ................................................................. 67

Figure 4-4: Components of a fuzzy inference system ........................................................................ 68

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Figure 4-5: Mamdani fuzzy inference method ..................................................................................... 70

Figure 4-6: Defuzzification schemes to derive a crisp output .......................................................... 72

Figure 5-1: Voltage profile across a 132 kV insulator string ............................................................. 80

Figure 5-2: No rust insulator pin condition.......................................................................................... 81

Figure 5-3: Light rust insulator pin condition ...................................................................................... 82

Figure 5-4: Medium rust insulator pin condition ................................................................................ 82

Figure 5-5: Heavy rust insulator pin condition .................................................................................... 82

Figure 5-6: P-F curve for rust condition of insulator pin .................................................................. 83

Figure 5-7: Cutaway drawing of a normal type cap and pin insulator ............................................. 84

Figure 5-8: Cutaway drawing of an anti-fog type cap and pin insulator ......................................... 85

Figure 5-9: Chipped porcelain disc ........................................................................................................ 86

Figure 5-10: Small breakage of porcelain disc ...................................................................................... 86

Figure 5-11: Major radial breakage of porcelain disc .......................................................................... 87

Figure 5-12: Total porcelain disc breakage ........................................................................................... 87

Figure 5-13: Arrows show partially broken porcelain discs in an insulator string ........................ 87

Figure 5-14: Arrows show 2 totally broken porcelain discs in an insulator string ........................ 88

Figure 5-15: Structure of insulator inspection FIS .............................................................................. 92

Figure 5-16: Input membership functions for pin rust conditions .................................................. 93

Figure 5-17: Input membership functions for porcelain shell conditions ...................................... 94

Figure 5-18: Output membership functions for insulator condition............................................... 94

Figure 5-19: A 3-dimensional plot of insulator inspection FIS showing the relationship

between the two inputs and output ....................................................................................................... 98

Figure 5-20: Insulator Sample 1 .............................................................................................................. 99

Figure 5-21: Pin rust condition for insulator Sample 1 ................................................................... 100

Figure 5-22: Porcelain shell condition for insulator Sample 1 ....................................................... 100

Figure 5-23: Resultant of invoking Rule 6 ......................................................................................... 101

Figure 5-24: Resultant of invoking Rule 11 ....................................................................................... 101

Figure 5-25: Aggregation of Rules 6 and 11 resultants ................................................................... 102

Figure 5-26: Insulator Sample 2 ........................................................................................................... 103

Figure 5-27: Porcelain shell condition for insulator Sample 2 ....................................................... 104

Figure 5-28: Pin rust condition for insulator Sample 2 ................................................................... 104

Figure 5-29: Resultant of firing Rule 2 ............................................................................................... 105

Figure 5-30: Resultant of invoking Rule 3 ......................................................................................... 106

Figure 5-31: Aggregation of Rules 2 and 3 resultants ...................................................................... 106

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Figure 5-32: Insulator Sample 3 ........................................................................................................... 107

Figure 5-33: Porcelain shell condition for insulator sample 3 ........................................................ 107

Figure 5-34: Pin rust condition for insulator sample 3 .................................................................... 108

Figure 5-35: Resultant of invoking Rule 12 ....................................................................................... 109

Figure 5-36: Resultant of invoking Rule 17 ....................................................................................... 109

Figure 5-37: Aggregation of Rule 12 and Rule 17 resultants .......................................................... 110

Figure 5-38: Proposed structure of tower inspection FIS .............................................................. 119

Figure 5-39: TNB transmission line forced outages 1997-2003 .................................................... 121

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LIST OF TABLES

Table 2-1: Basic transmission structure types ........................................................................................ 9

Table 2-2: Requirement for zinc coating thickness as per BS EN ISO 1461 ................................ 11

Table 2-3: AN and RI limits for insulator at typical system voltages .............................................. 39

Table 3-1: ASTM C867:1999 criteria for corrosion of steel in concrete ......................................... 44

Table 3-2: Emerging and available aerial inspection technologies ................................................... 60

Table 4-1: Determination of membership function from α-cut sets ............................................... 66

Table 5-1: Category of in-service porcelain insulator disc defects ................................................... 88

Table 5-2: Pin corrosion description used by Powerlink during visual inspection ....................... 90

Table 5-3: IF-THEN rules used in the insulator inspection FIS ...................................................... 97

Table 5-4: Suggested insulator maintenance decision ........................................................................ 98

Table 5-5: Introduction of environmental coding for areas based on IEC’s pollution

severity classification .............................................................................................................................. 115

Table 5-6: Environmental coding for zinc loss ................................................................................ 116

Table 6-1: Summary of transmission line components, their functions and failure modes ..... 125

Table 6-2: Summary of TNB and Powerlink porcelain cap and pin insulator inspection

practice ..................................................................................................................................................... 127

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LIST OF ABBREVIATIONS

AAAC All Aluminum Alloy Conductor

AC Alternating Current

ACSR Aluminum Conductor Steel Reinforced

ACSS Aluminum Conductor Steel Supported

AGNIR Advisory Group on Non-Ionizing Radiation

AN Audible Noise

ANSI American National Standards Institute

ASTM American Society for Testing and Materials

BS British Standard

CIGRE International Council on Large Electric Systems

DC Direct Current

EMF Electromagnetic Field

EPDM Ethylene Propylene Diane Monomer

EPR Ethylene Propylene Rubber

EPRI Electric Power Research Institute

ESDD Equivalent Salt Deposit Density

FIS Fuzzy Inference System

FRP Fiber Reinforced Plastic

GTACSR Gap Built-in Heat Resistant Aluminum Alloy Conductor

IEC International Electrotechnical Commission

IEEE Institution of Electrical and Electronic Engineers

ISO International Standard Organization

NRPB National Radiological Protection Board

OPGW Optical Fiber Ground Wire

RBS Rated Breaking Strength

RCM Reliability-centered Maintenance

RF Radio Frequency

RIV Radio Interference Voltage

ROW Right-of-Way

RTV Room Temperature Vulcanization

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SiR Silicon Rubber

TNB Tenaga Nasional Berhad

UV Ultra Violet

ZTACIR Heat Resistant Aluminum Alloy Conductor Invar Reinforced

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STATEMENT OF ORIGINAL AUTHORSHIP

“The work contained in this thesis has not been previously submitted for a master degree

at any other tertiary educational institution. To the best of my knowledge and belief, this

thesis contains no material previously published or written by another person, except

where due reference is made.”

Signed: Mohd Junaizee Mohd Noor (Author)

Date:

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ACKNOWLEDGEMENTS

In the name of God ALLAH, the Most Gracious the Most Merciful

First and foremost I would like to start by acknowledging, praising and thanking my Lord,

Allah, the Glorified and the Creator of all things, for blessing me with the good health and

wellbeing throughout my one-and-a-half year of studies and for making it a reality for me

to complete this thesis.

I would like to thank my academic supervisor, Associate Professor David Birtwhistle, for

his guidance and support during all the phases of this research degree. Throughout the

crests and troughs of the study, he was always there with invaluable technical advice and

assistance.

I would also like to state my sincere appreciation to my associate supervisor, Dr. Stewart

C. Bell of Powerlink Queensland, for sharing his expertise and thoughts as well as acting

as a sounding board for ideas, ensuring I knew exactly what I was talking about.

In addition, I would like to thank Ian Nichols and Maurice Donnelly from Powerlink

Queensland and Azmi Abdullah and Rafida Othman from TNB Malaysia for their

assistance during the data gathering phase of this study.

Thanks also to TNB Malaysia for providing me with the financial support and

opportunity to further my studies in this beautiful country of Australia.

Last and by no means the least, I would like to express my special gratitude to my

wonderful wife and colleague Dr. Farah Inaz Syed Abdullah, my lovely daughter

Esmeralda Noor, and my immediate family for their patience, compassion and

encouragements while pursuing this study. Hopefully they will get to see more of me, now

“it” is finished.

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CHAPTER 1: INTRODUCTION

1.1 Justification for and Introduction to the Research

High voltage transmission lines play a very important role in a power system. Apart from

their primary function of transferring power at high voltages from generating stations to

load centers, through various interconnections in the network, they provide the means for

effective, safe, economic and reliable operation of a power system. A transmission line

system consists of the following major components:

Tower or structure

Foundations

Conductors and earth wires

Insulators

Each of the components has its own functions in order for the transmission line to

operate. Failure of any of these components may render the transmission line inoperative.

Transmission line failures are highly undesirable in a power system because such failures

usually result in power interruption of a large area.

In order to ensure the healthy operation of transmission lines, power utilities conduct

periodic inspection and diagnosis of transmission line components. The main objective of

conducting inspection and diagnosis is to locate component defects so that appropriate

maintenance actions can be taken before the defects develop into catastrophic failures.

During the literature review, it was found that, apart from manual visual inspection, there

are other various methods of transmission line component inspection and diagnosis that

are currently available. Depending on the component, some of these methods utilize

equipments that detect characteristic parameters that are both electrical in nature, such as

voltage, current, corona and magnetic fields, and non-electrical in nature, such as vibration

and temperature. These equipments, however, were found to be either expensive, still in

development stages or highly affected by environmental factors during field operation.

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However, in a recent survey on 90 utilities around the world conducted by CIGRE [1] and

a survey on Australian and New Zealand utilities conducted by McMahon [2], it was

discovered that the most common utility practice for locating component defects on

transmission lines is by visual inspection. Depending on which part of the tower that

needs to be assessed, visual inspection is conducted by line inspectors either from the

ground, from the top of the structure (by climbing the structure) or from the air (by

observing from fixed wing aircrafts or helicopters) [3].

Field experience indicates that there are drawbacks associated with visual assessment of

transmission line defects. These include:

Defect levels are expressed qualitatively using vague and linguistic terms such as

“medium crack”, “heavy rust”, “small deflection” and “large breakage”. These

statements vary from one inspector to another depending on the inspector’s

inherent knowledge, reasoning, experience, health and fitness levels, the

environment wherefrom the inspection is made and whether visual aids are used.

The assessment of the defects is based solely on the inspector’s personal judgment

making the inspection results highly subjective. Many a time the maintenance

engineer requires a second inspection by a more experienced inspector to confirm

the extent of the defect.

As a result, there is a large variance and inconsistency in defect level reporting

which, in time, makes it difficult to monitor the condition of the components.

This leads to the utility making ineffective or wrong maintenance decisions and

inefficient control of maintenance and repair expenditures.

The aim of this research is to develop a decision support tool that will primarily address

the problem of inherent human bias and subjective judgment that prevail during

transmission line inspection and assessment. The hypothesis that provides the motivation

for the study is that if such a tool is able to reduce or remove the uncertainty associated to

inspector visual assessment during field inspection, then the information gathered from

field inspection can be used effectively by the utility to manage its maintenance actions.

The tool is in the form of a standardized inference system utilizing fuzzy logic. Fuzzy

logic, as introduced by Zadeh in 1965 [4], is a form of artificial intelligence technique that

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was specifically developed to handle the intrinsically fuzzy human thinking, reasoning,

cognition and perception process.

This thesis presents the development, application and results of a knowledge-based fuzzy

inference system (FIS) on utility inspection practice of assessing one of the most

important transmission line components, porcelain cap and pin insulators. The FIS is

designed using Matlab’s proprietary Fuzzy Logic module. Data regarding insulator

inspection practice and sample defective insulators on which the system is tested are

obtained from TNB Malaysia and Powerlink, the company responsible for the operation

and maintenance of transmission network in Queensland, Australia. The results of

applying the FIS show that not only can it reduce the subjectivity associated with visual

inspection of insulators but it can also be used to assist engineers in making strategic

decisions such as ‘replace immediately’, ‘flag for next maintenance cycle’ or ‘do nothing’.

The success of applying the FIS on insulator visual inspection provides the impetus for

applying the same design principle of the FIS on other components of the transmission

line which are also normally subjected to visual inspection.

The major contribution of the research is the presentation of a methodology that

improves the visual inspection of transmission line components. The methodology uses a

knowledge–based decision support tool. Such a tool can be programmed into mobile

electronic devices such as laptop computers or handheld personal digital assistants

(PDAs) for field use. The novel methodology can facilitate the visual inspection process

by:

Making available expert knowledge in the field

Reducing the inherent uncertainty faced by field inspectors

Providing a facility to store defect data in a computerized system for defect

analysis and condition monitoring

Enabling effective use of field data for use with modern maintenance policies

such as Condition-based Maintenance or Reliability-centered Maintenance

Another contribution of the research is the development of a mathematical model that

utilizes the output of the inspection program to plan for bulk maintenance of transmission

line components. To the best of the author’s knowledge, such a methodology has not

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been investigated before and it is hoped that the research will provide utilities with a better

method of maintaining its transmission line assets.

1.2 Aims and Objectives of the Research

The research was conducted to achieve the following objectives:

To identify the main components of transmission lines, their various designs, and

their functions

To understand their failure modes that lead to loss of functions including factors

that affect their operation

To review the inspection and maintenance practices of utilities around the world

To identify the problems associated with utility practice of manual visual

inspection

To study the principles of fuzzy logic and fuzzy inference system

To design, apply and test a knowledge-based fuzzy inference system applied on

the visual inspection of porcelain cap and pin insulators

To investigate the use of the system as a decision-support tool for maintenance

management of transmission lines

1.3 Organization of the Thesis

Chapter two provides an introductory account of the components of transmission lines,

their functions and failure modes. Factors that can affect the operation of transmission

lines in a power system are discussed. Mitigation steps that are currently taken by utilities

to address these problems are also deliberated. Once the failure mechanisms are

identified, it is necessary to appreciate the available methods of inspection and

maintenance as practiced by the utilities. This is presented in the following chapter.

Chapter three is divided into two parts. The first part of the chapter looks into the

available methods of inspection and diagnosis of transmission line components that are

reported in various literatures. The advantages and disadvantages of these methods, some

of which utilize equipments that detect parameters such as voltage, current, noise,

vibration and temperature, are discussed. The second part of the chapter reviews the

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inspection practices and maintenance strategies as conducted by utilities worldwide. The

results of recent industry surveys conducted by CIGRE and McMahon, which indicate

that worldwide utility practice of locating transmission line defects is by visual inspection,

are elaborated on. The chapter finally discusses the difficulties faced by the inspectors

when making visual inspection and the problems faced by utilities that use visual

inspection to manage its maintenance actions. This leads to the need for a decision

support tool that can be used to address the uncertainties that are associated with visual

inspection. This type of uncertainty can be best handled by the artificial intelligence

technique of fuzzy logic.

The next chapter provides background information on fuzzy logic which will be used later

in the proposed decision support tool to assist visual inspection of transmission line

components. The main elements of a FIS, namely membership functions, fuzzy IF-

THEN rules, and defuzzification methods, are described here. Several example

applications of fuzzy logic in the utility environment that are available in the literature are

also discussed and commented on.

Having understood the principles of fuzzy logic and its applications, chapter five details

primarily the design, development and application of a knowledge-based FIS applied on

utility visual inspection practice to locate defects on porcelain cap and pin insulators. A

closer look at two types of porcelain insulator failure mechanisms – corrosion of the pin

and breakage of porcelain shells – are firstly presented. Current insulator visual inspection

practices of the two utilities (TNB and Powerlink) to locate the above defects are then

discussed, highlighting the associated problems. The chapter then proceeds to explain the

design of the insulator inspection FIS and its simulated application on actual in-service

insulators taken from both the utilities. The results of this application are then discussed,

indicating how the FIS assists in achieving consistency in assessing insulator defects. It is

shown next how the output of the FIS can be used to assess multiple insulators in a string

and also multiple insulator strings in a transmission line. It is also shown how the output

of the FIS can be further used in a proposed mathematical model to estimate the

appropriate date for bulk replacements of insulators. The chapter finally discusses the

application of the knowledge-based FIS on the visual assessment exercise of other

components of the transmission line.

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Lastly, chapter six details the conclusions derived from the research contained in this

thesis. Future work, which the author feels may prove particularly beneficial, is also

elaborated on.

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CHAPTER 2: COMPONENTS OF TRANSMISSION LINES AND THEIR FAILURE MODES

2.1 Chapter Overview

This chapter presents an overview of the functions of transmission line components and

their failure modes relevant to the research described later in this thesis. The major

components of high voltage transmission lines as covered in this chapter are as follows:

Towers and structures

Foundations

Conductors and earth wire (including joints)

Insulators

Each of these components has its own functions that enable power to be transmitted

safely and reliably through transmission lines. Failure of a transmission line component as

outlined in this thesis is defined by the loss of its functions. This chapter discusses the

design of the components, their features and, most importantly, how they fail in

operation.

It is highlighted in this chapter that functional failures of transmission line components

are generally gradual and can be of electrical, structural or mechanical in nature. It is also

highlighted that these degradations are primarily influenced by environmental factors such

as local climate, weather, elevation and ambience wherein the transmission line is built.

Mitigation steps that utilities take to address these problems are also deliberated in this

chapter.

2.2 Towers and structures

Figure 2-1 shows a typical single circuit 765 kV extra high voltage transmission line and its

components commonly used in North America. Notice that the transmission line has two

earth wires (denoted by “shield conductors” in the figure) strung at the top of the

structure and the bundle conductors are held by v-string insulators.

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Figure 2-1: Example of a typical overhead transmission line (Source: [5])

2.2.1 Functions of transmission towers

The main purpose of the transmission tower is to carry the overhead transmission line

conductors and earth wires above the ground. In fulfilling this role, it has to:

withstand all the variety of forces that it is exposed to with regards to the

environment it is located. These forces include normal still air loads, extreme wind

loads, ice loads, loads during erection and maintenance, and the changing of

conductor sag when the conductor expands and contracts with normal daily

current loads [5]. In certain instances, tower designs must withstand loads

imposed by extreme conditions such as in earthquake, cyclone and tornado areas

[6].

maintain electrical clearances between live conductors and any earthed body in the

vicinity of the tower such that the energized lines do not induce any hazardous

voltage that could render the operation of the transmission lines dangerous to the

environment and the public.

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provide a path to earth fault and/or lightning current so that any danger to the

environment as a result of the two occurrences is reduced. Hence, the tower must

also exhibit low resistance to ground during transient lightning over voltages.

Depending on network requirements of a power system, the tower may be designed to

cater for a single 3-phase circuit, double 3-phase circuits, multiple voltage circuits, and,

with direct current transmission, either monopolar or bipolar construction. In certain

countries, due to land constraints, new transmission lines are always built on double

circuit towers and old single circuit lines are upgraded to double circuits to optimize the

use of land easements.

There are basically six different types of transmission line structures in common use

worldwide with many design variants based upon them. Table 2-1 lists the basic structure

types [7]:

Structure Type Description

Self-supporting

lattice steel tower

Most common design style, easiest to work on but

makes the largest visual impact. Relative medium cost.

Lattice guyed steel

tower

Simple foundation but requires large easement width to

accommodate guys. Lowest capital cost.

Steel/concrete

monopole

Slim appearance, low maintenance for galvanized steel

and concrete. Small easement width. Applicable in

urban areas where easement acquisition is expensive

and aesthetic visual impact is required. Highest capital

cost.

Compact

structures

Special adaptations of the foregoing types to meet

specific space or environmental limitations

H-frame in either

wood pole or

concrete

Traditional rectilinear design, best suited to single

circuit use. Requires wide easement.

Table 2-1: Basic transmission structure types

The selection of the type of structures to be used in a transmission line is mainly affected

by the environment where the structures are located. These factors are [7]:

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Pollution level: whether the route of the transmission line is going to cross highly

polluted and corrosive area or environmentally sensitive country which requires

specific designs or surface treatment of the structures on part or all of the route

Terrain: whether there is difficult terrain where the line trespasses which makes

the structures susceptible to problems of land slides, floods or tidal inundations,

high soil resistivity, ‘bottomless’ sand, exposure to wind, lightning or other natural

phenomena

Aesthetic: whether the visual impact of the transmission line can be improved to

make it more acceptable to the public eye by using either a compact design or a

monopole

Climatic: whether the transmission line has to operate in extreme climatic

conditions such as regular high winds, extremes of ambient temperature

(including snow and ice) or marine salt sprays

Current load: whether the transmission line is meant to be operated during

emergency loading conditions within the transmission network

Maintenance requirements: whether maintenance on the transmission line can be

done live or de-energized, with climbing, using elevated platform vehicles or

helicopter

Figure 2-2 shows an example of 2 types of tower structures which carry 220 kV

transmission lines located side-by-side.

25

Figure 2-2: Monopole and steel lattice tower designs for 220 kV transmission lines (Source: [8])

Under normal circumstances, utilities worldwide use steel lattice transmission tower as the

structure of choice due to its cheap installation cost and relatively low operational

maintenance cost compared to the other types. To make the steel tower resistant to the

corrosive effects of the environment, the steel lattice members are treated in a process

called ‘hot-dip galvanisation’ which is to provide a layer of zinc by dipping the members

into a zinc bath. The zinc coating acts as a sacrificial element to protect the steel from

being oxidized or developing rust. One of the international reference standards for

coating thickness is based on BS EN ISO 1461:1999 [9] which requires coating thickness

of the steel members as per Table 2 below:

Steel thickness Minimum average coating

weight, G/M2

Coating thickness,

ΜM

1 mm up to 2 mm 335 47

2 mm up to 5 mm 460 65

Table 2-2: Requirement for zinc coating thickness as per BS EN ISO 1461 [9]

26

2.2.2 Failure modes of steel transmission towers

Steel transmission towers are typically designed and built to last for 60-70 years of

operation. They are considered to have failed when they can no longer serve their

function which is primarily to withstand all the forces that they are subjected to within the

environment they are located. Tower buckling and/or collapse are a sign of the tower

losing its function.

Tower failures can be attributed to either:

aging

natural disasters (such as earthquakes, wind/ice/snow storms, floods, landslides

and foundation erosion)

vandalism or sabotage

Failures due to natural disasters and vandalism are relatively rare and difficult to control,

although the damage they cause to the transmission line can be quite extensive. Figure 2-3

shows a series of tower failures in Canada due to an ice storm.

Figure 2-3: Structural failures of a transmission line due to an ice storm in Canada (Source: [10])

27

The single factor that affects the integrity of steel transmission towers is steel corrosion

[11]. Corrosion creates weak points on the tower members. When the effects of rust on

the tower members reach a point whereby the members cannot withstand the mechanical

strength they are designed for, the tower can buckle and, at worst, collapse. What is more

serious is that the cascading effect of one tower collapse can bring down several other

interconnected towers thus rendering the whole transmission line circuit unserviceable.

Factors that influence the rate of corrosion include:

Relative humidity

Atmospheric presence of corrosive agents (e.g. sulphur dioxide or nitrogen oxide)

Age of tower

Corrosion is usually more evident at the base of the transmission tower where there is

contact with ground.

2.3 Foundations

2.3.1 Functions of foundations

The purpose of tower foundation is to transmit to ground the mechanical loads that are

due to the tower and conductors so that the loads do not cause any settlement or

movement which would affect the stability of the tower [5]. The loads are the resultants of

the dead weight of the tower and the wind forces that act upon the tower and conductors.

These forces result in uplift and compression forces which is transmitted to the tower legs

to the foundation. Therefore it is important that the foundation is designed correctly to

resist these forces in order to ensure the stability of the tower structure. Figure 2-4 shows

the direction of uplift and compression forces imposed on tower foundation based on

wind direction acting upon the tower.

28

Figure 2-4: Uplift and compression forces on tower foundations

Tower foundation design needs to consider the properties of the soil on which the tower

is standing. These are determined by conducting soil investigations which are performed

in situ and the soil properties are analysed in the laboratory. The soil property

classifications are based on measurements of grain size, density, shear strength,

compressibility, chemical composition and moisture content [12]. There are basically four

types of foundation design used for tower structures:

Steel grillage

This type of foundation is used where the upper layers of soil have relatively low

bearing capacities. It serves to evenly distribute the load in order to avoid bearing

capacity failure and to ensure any ground settlement is evenly catered for. Steel

reinforcement bars are built into the concrete cast to increase its strength. It is also

commonly referred to as the ‘pad and chimney’ foundation due to the shape of its

design. Figure 2-5 shows a typical steel grillage foundation:

Wind direction

UpliftCompression

Tower

Ground line

Foundations

29

Figure 2-5: Typical construction of transmission tower steel grillage foundation

Pile foundation

This type of foundation is necessary for very poor soils. It transmits the load to

the lower ground layers which are more stable. Several piles may be required for

each tower foundation depending on the load capacity of the piles. A few types of

piles are normally used which include steel sections, precast concrete sections, and

steel screw anchor [12]. There is also the bored, cast in situ concrete pile in which

steel casings are removed after the concrete is cured.

Strip footing [12]

These are used in good denser soils with high bearing capacity so that the weight

of the structure is transferred adequately to the ground. Its construction is similar

to the steel grillage foundation but with a much smaller pad.

Anchor foundation

In areas where there is very hard or rocky ground, it may not be economically

feasible to excavate and construct concrete foundations. Instead, the stability of

the rock ground is used as a base for the tower leg. This is done by grouting the

steel reinforcement bars into predrilled holes in the rock. Figure 2-6 below shows

a typical tower rock anchor foundation.

Groundlevel

Steel tower leg member

Steel grillage

Concrete pad

Concrete chimney

30

Figure 2-6: Typical construction of transmission tower rock anchor foundation

2.3.2 Failure modes of foundations

The foundations of a transmission tower are deemed to fail when they can no longer

transfer the tower loads to ground. The effects of the foundation failure could bring the

tower down as the foundations can no longer withstand the compression and/or uplift

forces they are subjected to.

It has been identified that there are two major failure modes of transmission tower steel

grillage concrete foundations:

failure of the foundation itself due to corrosion of steel reinforced bar

excessive earth movement due to natural disasters such as earthquakes, floods,

landslides and erosions

Steel corrosion in concrete is a specialized subject in civil engineering. The mechanism of

steel corrosion in concrete is generally associated with the loss of the protective action

provided by the cement [13]. Concrete exhibits alkaline properties and fresh concrete

mixture typically registers pH 13 [14]. Through micro cracks in the concrete, this alkaline

property can turn to acidic (pH < 9) either through the action of atmospheric carbon

dioxide (“carbonation”) or by penetration of aggressive ions such as chloride. In the

Ground level

Steel tower leg member

Soil layer

Rock layer

Steel grillage

Concrete cast

31

presence of oxygen and moisture, these ions react with the steel bar to form bulky rust,

which can exert sufficient forces to form cracks in the concrete. This leads to further

ingress of moisture and the reaction is accelerated until the concrete ultimately crumbles

leading to failure of the foundation. When the foundation can no longer bear the uplift

and compression forces exerted by the tower, the tower then collapses.

Corrosion, fortunately, is a slow-moving process. It is important to detect signs of steel

corrosion in foundation early and to take preventive measures before it reaches a state of

imminent foundation failure.

Foundation defects due to earth settlement can be remedied if the settlement process is

gradual and defects are detected early. Remedial actions to contain earth movement such

as building a retaining wall, creating proper drainage and adding vegetation to top soil

would help alleviate this problem.

However, foundation failures as a result of excessive earth movements due to natural

disasters like floods, earthquakes or land slides would normally be catastrophic. Some

examples of the extent of such occurrences in several countries are given below:

In January 1994, an earthquake measuring 6.6 on the Richter scale hit Los

Angeles, California. This earthquake resulted in damage to a section of a four-

circuit 230kV line supplying 1500MW to the city of Los Angeles. Due to this

incident, electricity supply to the whole city was interrupted [15].

In 1991, Mount Pinatubo in the Philippines erupted after lying dormant for 600

years. Lahar (mixture of volcanic ash and water) flow from the mountain

destroyed a section of a 220kV transmission lines that were supplying 1200MW

power to the city of Manila [16].

In October 1999, a massive flood hit the state of Orissa in India [17]. The flood

swept a few towers of the transmission line that was supplying power to its capital

city, Bhubaneswar.

32

2.4 Conductors and earth wires

2.4.1 Functions of conductors and earth wires

The main function of transmission line conductors is to carry rated current up to their

design temperature within mechanical design limits. In satisfying this purpose, conductors

also need to maintain even sags throughout the line route so that the clearance between

the ground and the conductors are within statutory limits.

There are 2 conductor types that are currently in use by transmission utilities worldwide.

They are:

ACSR conductor (Aluminium Conductor Steel Reinforced)

This conductor type gets its name from its construction which is galvanized steel

strands surrounded by aluminium steel strands. The steel strands provide strength

whilst current is conducted through the aluminium strands. A layer of grease is

normally applied on the steel strands to provide extra corrosion protection. Figure

2-7 shows typical ACSR stranding arrangements.

Figure 2-7: Typical ACSR conductor stranding arrangements (Source: [18])

33

AAAC conductor (All Aluminium Alloy Conductor)

This type of conductor is made up of strands of aluminium alloys. These alloys,

often containing silicon and/or magnesium, provide high breaking strength and

good electrical conductivity [5]. 2 alloys that are commonly used to manufacture

AAAC conductors are alloy 1120 and alloy 6201 [19]

The ACSR conductor is very widely used because of its mechanical strength, widespread

manufacturing capacity and cost effectiveness [12]. However, the AAAC conductor is

increasingly being used in place of ACSR due the advantages which are outlined below

[19]:

Higher conductor and line ratings: for the same outer diameter, AAAC

conductors can have from 9% to 14 % higher current ratings compared to ACSR

conductors.

Higher strength-to-weight ratio: AAAC conductors have up to 35% higher

strength-to-weight ratio compared to ACSR conductors. Hence, AAAC

conductors provides for smaller sags and lower tower heights [12].

Less capital cost: for the same stranding configuration, AAAC conductor costs

15% lower per meter compared to ACSR .

Lower I2R losses: the lower AC resistance of AAAC gives savings in operating

cost over ACSR .

Better corrosion resistance: ACSR conductors are more prone to corrosion related

problems due to the presence of steel in its stranding.

Easier jointing procedure: jointing of ACSR conductors requires compression

procedures on 2 sleeves whereas for AAAC only 1 jointing sleeve is needed.

The disadvantages of using AAAC conductor are that it has lower breaking load capacity

and limited self-damping properties compared to ACSR.

One of the challenges faced by utilities today is to transmit more power from generating

stations to existing load centres in the most cost-effective way. One way to achieve this

objective is to retrofit old towers with higher capacity conductors rather than constructing

new transmission lines. The alternative conductor types that are available today are [20]:

34

ACSS conductor (Aluminum Conductor Steel Supported)

ZTACIR conductor (Heat Resistant Aluminum Alloy Conductor Invar

Reinforced)

GTACSR conductor (Gap Built-in Heat Resistant Aluminum Alloy Conductor)

These conductors are electrically and dimensionally very similar to ACSR conductors

except that they have lower heat expansion coefficient than that of ACSR conductors.

This results in them being able to operate at higher temperature but with no change in

sag, which in turn brings about higher current carrying capacity.

When the conductor is energized at high voltages, the air surrounding the conductor

becomes ionized. When this ionization process exceeds the air’s dielectric breakdown

strength, electrical discharges at various intensities along the conductor occurs. This

phenomenon is termed ‘corona’ [21] and can be visually characterized by a luminous glow

given the right atmospheric conditions. Corona is the source of radio interference (RI)

signals (unwanted electrical signals that may affect radio transmission or navigation

equipment [5]) and audible noise (AN) – the crackling or buzzing sound often heard when

someone stands in the vicinity of transmission lines.

Energized conductors are also the source of electric and magnetic fields (EMF) under the

line. There have been public concerns over long-term health effects of prolonged

exposure to EMF. Many scientific studies have been done to look into the possibility of

developing cancer but, to date they have not conclusively proven any causal link between

EMF and any health effect. In 2001, a report [22] was released by the Advisory Group on

Non-Ionizing Radiation (AGNIR) working for the National Radiological Protection

Board (NRPB) in the UK. The report found some evidence to suggest that prolonged

exposure to EMF at relatively high average levels (4 milliGauss (mG) magnetic field or

greater) may be associated to an increased risk of childhood leukaemia. However the

report also said that the evidence is “not strong enough to justify a firm conclusion that

such fields cause leukaemia in children”. Furthermore, measurements near power lines

have found [23] that the highest magnetic field reading is only around 2 mG.

35

Earth wires (sometimes referred to as shield conductors) are strung at the topmost part of

the transmission tower. Under normal operations, they do not carry any current but under

fault conditions, they must be able to withstand the fault current levels for which the

system is designed. Earth wires are designed to intercept lightning strokes so as to restrict

lightning surge voltage rise on phase wires and reduce the risk of insulator flashovers.

Earth wires are normally smaller in size compared to phase conductors. They can be made

of the same material as the phase conductors i.e. ACSR or AAAC.

Optical Ground Wire (OPGW) [7] is a specially constructed earth wire with a protective

tube into which optical fibre strands are layered. The optical fibre is used for

communication, control and information transmission purposes.

Conductors have limited length, particularly for manufacturing convenience and transport

purposes. Therefore, many segments of conductors are connected to make up a

transmission line. Connections between lengths of conductors are made manually at site

by using a sleeve that is compressed on the conductor with a hydraulic press. The

function of the joint is primarily to achieve the electrical and mechanical continuity of the

conductor. Hence, the operational performance of a transmission line is affected by

factors that influence both the conductor and joints.

2.4.2 Failure modes of conductors

Conductor failures are highly undesirable because when conductors break, they affect

system operations, supply reliability and public safety. The common failure modes for

conductors are due to:

Corrosion

Vibration fatigue

Annealing

The factors affecting the condition of conductors which can result to the failure modes

above include:

Conductor loading

Manufacturing and installation quality

36

Material stress (due to tension or vibration)

Protection (application of grease on steel core of ACSR conductors)

Weather

Levels and types of environmental pollution

Age

2.4.2.1 Conductor corrosion

Shannon [24] explains that for ACSR conductors, there are two predominant mediums

that result in corrosion problems:

Industrial pollution

Salt spray from the sea

Corrosive industrial pollution is carried to the conductor in the form of rain, fog or mist.

The corrosive effluence then reacts with the galvanising layer of the steel strand which is

eventually depleted. When this happens, oxidation of the steel core takes place and rust

forms on the steel core. The presence of rust eventually leads to the steel core losing its

mechanical strength. Typically, there is little or no loss of aluminium in this process and

therefore no external indication of conductor deterioration is visible until the conductor

fails. The arrow in Figure 2-8 shows evidence of rust on the steel core of an ACSR

conductor which can be characterised by the rough and pitting appearance.

Figure 2-8: Arrow showing evidence of rust on steel core of ACSR conductor (Source: [10])

37

Corrosion due to salt spray usually occurs where the transmission lines are close to the

coastal area. In this case, corrosion of the conductors mainly occurs as a result of galvanic

corrosion. Figure 2-9 shows a diagram of galvanic corrosion mechanism in an ACSR

conductor:

Figure 2-9: Sectional view of ACSR conductor illustrating galvanic corrosion mechanism (Source: [24])

In Figure 2-9, salt spray from the sea combined with precipitation collected on the

conductor creates an electrolytic circuit between the aluminium and the steel strands. The

galvanizing layer then becomes depleted thereby creating direct contact of steel and

aluminium. In the presence of the electrolyte, an electrolytic cell is then set up between

the steel and aluminium strands, with the aluminium becoming the sacrificial anode. This

results in loss of aluminium and creates high electrical resistance of the conductor at the

affected location. Failure of the conductor may result from loss of aluminium strands

which can melt because of high temperature due to increase in resistance to current flow.

In this process, evidence of aluminium loss can be detected by the presence of bulky

white powder (AlCOH) on the surface of the conductor. At the same time, conductors

develop hotspots at locations where there is high resistance.

Conductor mid span joints are given particular attention by many utilities because

corrosive effluence as mentioned above tends to concentrate at the joints [24]. Marshall

[25] pointed out that although joints appear homogeneous, they actually have micro-voids

which collect water, corrosive contaminants and oxygen that can accelerate the corrosion

process. Corrosion on joints can be of three types:

38

Pitting corrosion: localised corrosion which forms holes on the surface of the

joint

Crevice corrosion: corrosion in confined crevices where acidic build up occurs

Corrosion (rusting/oxidising) of the steel sleeve and strands inside the joint

Marshall also suggested that the effect of corrosion at the joints leads to an increase in

joint resistance long before they develop high temperature and eventually fail.

The probability of the conductors or joints developing corrosion problems is higher at the

lowest point of the span since it takes the longest for the conductor to dry out at this

position and it is also easier for the corrosive effluence to collect at this point [24]. The

arrow in Figure 2-10 shows signs of corrosion at a conductor mid span joint:

Figure 2-10: Arrow showing signs of corrosion at a conductor mid span joint (Source: [26])

2.4.2.2 Conductor vibration

Conductor failures can also occur due to the generation of vibration in the conductors as

a result of wind action. There are three main forms of conductor vibration:

Aeolian vibration

Galloping

39

Wake-induced sub-conductor oscillation

Aeolian vibration is generally caused by light steady wind (between 1 to 8 m/sec) blowing

between 0o to 75o to the conductor axis [5] which results in the formation of vortices on

the other side of the conductor. These vortices shed alternately from the conductor top

and bottom and when this happens synchronously along the conductor length, it vibrates.

The frequency of aeolian vibration is usually in the range of 5 to 100 Hz and with

amplitudes of 0.5 to 2 conductor diameters.

If uncontrolled, aeolian vibration can lead to early fatigue of the conductor by causing

fretting between strands of the conductor adjacent to clamps or fittings. This can

ultimately lead to fatigue crack initiation and conductor breakage. Figure 2-11 below

shows multiple fatigue cracks on the outer most strands of an ACSR conductor.

Figure 2-11: Multiple fatigue cracks of outermost aluminum strands of an ACSR conductor (Source: [10])

Aeolian vibration is more likely to occur on transmission lines located in open terrains

where there is no shielding from winds. There are several methods that can be used to

mitigate aeolian vibration problem. The use of Stockbridge vibration dampers fixed at

points where antinodes form during conductor vibration is one of them. Another method

is to string the conductor at a relatively low tension as it is found [5] that if the conductor

tension is maintained at less than 20% of the ultimate tensile strength, the likelihood of

vibration is significantly reduced. A recent CIGRE Task Force [27] suggested a new and

more precise approach of conductor tensioning based on the ratio of H (horizontal tensile

load derived at average temperature of the coldest month where the line is built) and W

(conductor mass per meter). The Task Force recommends values of H/W of about 1000

m in flat open grounds and up to 1425 m for areas with vegetation shielding.

40

Galloping is a low frequency phenomenon in which large amplitude mechanical

oscillations occur on conductors with a frequency of between 0.15 Hz and 1 Hz.

Galloping occurs when ice has formed on the conductors and where there are moderately

strong crosswinds of up to 45 km/h. The power transmitted by the wind is therefore

higher than for aeolian vibration. With the right angle of incidence, galloping can cause

the amplitudes to reach or exceed the sag of the conductor. In severe cases, phase-to-

phase conductor clashes can result. Galloping is also the cause of loosening or fatigue of

tower members. Galloping can be controlled by [28]:

Using interphase ties (restricts conductor clashing)

Using conductors with smooth body profiles (increase aerodynamics)

Heating the conductors by increasing electrical loading

Increasing conductor spacing

Wake-induced sub-conductor oscillation [12] (also called sub-span oscillation) occurs only

on bundled conductors and is produced by forces from the shielding effect of windward

sub-conductor on the leeward sub-conductor. Any vortices that are formed by the

windward conductor immediately expose the leeward conductor to a complex and

variable set of forces. These forces can either suppress motion or cause conductors to

move in various formations. Fortunately, problems associated with wake-induced sub-

conductor oscillation are not widespread. However, it can cause sub-conductor clashing,

fatigue of dampers or spacer dampers and damage to hardware fittings. These problems

can be mitigated by [29]:

Installing spacer dampers

Setting spacers at unequal intervals

Tilting the bundles at angles greater than 20o so that both sub-conductors are not

on the same horizontal plane

Increasing sub-conductor spacing

41

2.4.2.3 Conductor annealing

Annealing is caused by heating of a material and followed by a cooling period. During the

annealing process, the material experiences a change in its structure and for metals, this

result in a loss of tensile strength. This was proven by an experiment [30] conducted by di

Troia. In the study, a length of ACSR conductor was heated up to 175 oC in 100 cycles

and a series of tests conducted after the heating periods are over. The results showed that

the ACSR conductor experienced a reduction of material strength of up to 90% rated

breaking strength (RBS) (ANSI Class 1 standard requires withstand of up to 95% RBS).

Di Troia’s experiment showed that conductor problems associated with annealing occur

when the conductor is operated at beyond its design operating temperature. For ACSR

conductors, the aluminium strands stretch and when the conductor gets too hot (more

than 100 oC), the strands fail to retain their original shape and lose tensile strength. As a

result, transmission lines sag. Excessive sag due to reduced tensile strength, together with

high ice and/or wind loading events, can eventually lead to conductor tensile failure.

To operate conductors at higher temperatures without risking the problems associated to

annealing, special low-sag, high-temperature rated conductors should be used. One such

conductor is the ACSS (Aluminium Conductor Steel Supported) conductor. For the

ACSS, the aluminium strands are fully annealed at the factory and therefore will not

change its shape at high temperature operations. This prohibits the conductor from

further sagging and allows the conductor to be operated at high current loadings.

2.5 Insulators

2.5.1 Functions of Insulators

Insulators are used to attach the conductors to the cross arms of transmission towers.

They are the medium by which the live parts of the transmission line are separated from

the earthed parts. In doing so, they serve three purposes:

To provide mechanical support for the conductors under the worst loading

conditions

42

To withstand electrical stresses as a result of lightning, switching or temporary

over voltage surges that could initiate voltage flashovers under the worst weather

and pollution situations

To operate within interference (RIV and Audible Noise) limits

Insulators are therefore made of components consisting of dielectric material that gives

them the insulating properties and materials that provide mechanical strength. Insulators

are mostly recognized by their dielectric material, which is either of these three [31]:

Porcelain

Toughened glass

Polymers or composites

Porcelain and toughened glass insulators have been used for more than 100 years

worldwide while polymeric insulators were introduced as an alternative in the 1960’s [32].

Porcelain insulators are made of a mixture of clay materials which forms into porcelain

after being fired in a kiln. Glass insulators are usually made from soda-lime-silica glass

which is formed in a furnace and made toughened by rapid cooling. Composite insulators

are made from polymeric materials such as ethylene propylene dimethyl monomer

(EPDM), ethylene propylene rubber (EPR) or silicone rubber (SiR).

The most common type of porcelain and glass insulators is the cap and pin type where

one unit is attached to another with galvanized steel fittings to form a string. Figure 2-12

shows a typical cross-sectional view of a cap and pin insulator unit and Figure 2-13 shows

a complete porcelain cap and pin insulator string.

43

Figure 2-12: Cross-sectional view of a cap and pin insulator (Source: [8])

Figure 2-13: Porcelain cap and pin insulator string (Source: [8])

Polymeric insulators are always manufactured in one piece (commonly called long rod

insulators) with a fibreglass reinforced core to provide mechanical strength and polymeric

material cover attached to galvanized metal end fittings. Figure 2-14 shows a typical

polymeric insulator construction.

44

Figure 2-14: Typical polymeric insulator construction (Source: [8])

Two measurement terms always applied to insulators are: creepage distance, which is the

distance measured along insulating surfaces between the conductive parts, and arcing

distance, which is the distance in air between the conductive parts that normally have

operating voltage between them [33]. Selection of creepage distance on insulators is

determined by environmental factors (insulators with longer creepage distance are used in

highly polluted environments) whereas arcing distance is determined by line voltage and,

hence, the higher the voltage, the longer the insulator string. Depending on the design of

the transmission tower, insulators are strung in either horizontal, vertical or v orientation.

The voltage across an insulator string is not uniform. Voltage at the line end of the string

may constitute up to 15% [5] of the transmission line voltage and progressively decreases

towards the earth end of the string. Therefore, the electrical stress is higher at the line end

of the insulator string. On extra high voltage lines, grading rings [31] are used at the live

end of the insulator to mitigate this phenomenon.

Between the porcelain cap and pin insulators, utilities normally have equal preference to

use on transmission lines as they tend to have the same operational quality and cost [5].

45

However, polymeric insulators have been increasingly used by utilities due to the

following advantages according to surveys conducted by Schneider et al [34], Kikuchi et al

[35] and Philips [36]:

90% weight reduction

Lower installation cost

Aesthetically more pleasing

Resistance to vandalism

Improved handling of shock loads

Good contamination performance

The disadvantages of polymeric insulators are [31]:

More susceptible to ageing

More prone to handling and storage damage

Attract pecking birds

Contamination performance can change with time

2.5.2 Failure modes of Insulator

When put in service on transmission lines, insulators are considered to have failed when

they can no longer serve the purposes mentioned in section 2.1.4. Depending on factors

such as temperature, humidity, contamination, rain, mechanical loading, and voltage, the

failure modes can either be of mechanical or electrical in nature.

2.5.2.1 Mechanical failures

For cap and pin insulators, mechanical failure can result from failure of the pins due to

either corrosion or metal fatigue. The result of this phenomenon is pin fracture leading to

a conductor drop. The consequences of a conductor drop can be serious as, in addition to

loss of supply, if the transmission line is constructed across populated areas, damage to

public properties and/or death or injury to the public may be imminent.

Pin corrosion, according to tests carried out by NGK Insulators [37], is caused by

electrolytic action of the steel material with environmental contaminants catalysed by

46

leakage current. Gorur [31] further explains that the small mass and slender design of the

pin can lead to high levels of leakage current density on the pin surface which, in time, can

cause considerable amount of corrosion. This explains why evidence of rust is normally

found at the point where the pin is closest to the cement, the place where leakage current

is highest, as shown in Figure 2-15:

Figure 2-15: Pin corrosion is most severe at cement interface (Source: Author’s personal collection)

It can be observed from the author’s field experience in high voltage transmission line in

TNB Malaysia that:

Pin corrosion is more evident on suspension strings compared to tension strings.

This is due to the washing effect of direct rainfall on to the pin on tension strings.

Corrosion rate is not constant with time

Pin corrosion is more severe on insulators situated in the middle of the string

compared to the ones at the extreme ends

Rate of pin corrosion is higher in highly polluted environments such as in coastal

or industrial areas

To counter the effect of corrosion, most utilities use cap and pin insulators with sacrificial

zinc sleeves around the pin to inhibit the growth of corrosion a few years and thus

prolonging their useful life. The zinc sleeves work as sacrificial anodes.

47

Corrosion on the cap, although noticeable, has minor effect on the mechanical integrity of

the insulator due to its larger cross sectional area and lower leakage current density.

The insulator pin can also be damaged by metal fatigue. Aeolian vibration on the

conductor which is induced by incidental winds can be transmitted to the line hardware

and the insulators. Under these conditions, the pins, being the smallest part of the

insulators in a string, are subjected to high alternating mechanical stresses. This coupled

with the high tensions that pins are subjected to in a string, can cause eventual pin

fracture. Fractures of the cap do not usually occur because of its bigger volume and higher

mass.

Mechanical failures of polymeric insulators are always related to damage of the core. It can

be recalled that the core of polymeric insulators is made of fibreglass reinforced plastic

(FRP). Studies and experiments by many researchers have suggested that the reason for

this failure is due to the chemical degradation of the FRP as a result of reaction with

environmentally-induced acids [36] [38] [39] , or atmospheric humidity [40]. An IEEE

Task Force was formed to study this phenomenon and found out that brittle fracture can

be avoided if a good covering or seal is provided to prevent moisture from penetrating the

core [41]. Figure 2-16 shows the results of a brittle fracture on 2 polymeric insulators:

Figure 2-16: Brittle fractures on composite insulators (Source: [42])

Mechanical failure of the fibre glass rod of composite insulators can be due to poor rod

manufacture, mishandling during shipping, storage and installation, and operational

overloading [36].

48

2.5.2.2 Electrical failures

The electrical performance of insulators on transmission lines is determined by their

ability to prevent flashovers which can result in tripping of the lines. Flashovers occur

when the insulating medium, which is air surrounding the insulator string, breaks down

under electrical stress between the live and earthed parts of the transmission tower. Under

normal operating conditions, the insulator provides sufficient air-gap distance to prevent

flashover from occurring. However, several factors can effectively make this distance

‘shorter’ and initiate the flashover.

For cap and pin insulators, these include:

Loss of insulator shells due to:

o Vandalism [43]:

This problem is particularly troublesome for glass insulators. During

production of the glass shells, the hot and molten glass mixture is forced-

cooled in its mould by blowing cool air followed by dipping in cold water

baths. This action results in strong compressive forces at the surface of

the glass in balance with equally strong internal tension forces making the

glass shell toughened. If this balance is disturbed by a large impact, the

compressive forces on the surface will be released and the glass shell will

shatter to pieces. To some people, this shattering is a spectacular sight and

the glass insulators therefore often become targets for shooting practice.

This action reduces the number of glass insulator shells on the string

thereby reducing leakage distance which can lead to a line flashover. The

problem is not as serious with porcelain shells as a bullet will usually chip

or, sometimes, crack the shell. A small chip will not affect the insulator

electrically or mechanically but a large crack can initiate shell breakages

giving the same effect as glass insulators.

o Self shattering of glass shells [31]:

49

Self-shattering of glass insulators is a wear-in type of failure: the failure

incidents will reduce with time after installation, usually in 3 to 5 years.

The glass shells can shatter by itself due to impurities and air bubbles

trapped during the manufacturing process. Air bubbles result from not

enough fining agents used in the glass mixture. Impurities in terms of

stones and cords may also find their way into the glass mixture. Although

there are precautions that can be taken during manufacturing to contain

this problem, a number of imperfect insulators may be eventually used on

a transmission line.

The glass shells can also shatter due to erosion of the glass shells. Sodium

ions (Na+) which are contained within the glass shell will migrate under

DC voltage and heat. This ionic conduction may give rise to shattering of

the shells, particularly in highly polluted environments.

o Cement growth:

Cement growth is a term given to the phenomenon when dried Portland

cement, which is used to cement the porcelain shells to the cap and pin,

expands with time. Gorur [31] explains that there are three mechanisms of

Portland cement expansion which can occur in the cement mixture:

Delayed hydration of uncombined CaO to Ca(OH)2

Reaction of excess gypsum with tricalcium aluminate

Delayed hydration of periclase (MgO) to form brucite (Mg(OH)2)

The after-effect of cement growth is cracked porcelain shells and once this

happens, their usefulness as an electrical insulator is terminated. To

counter the problem, mortar is normally added to the cement mixture to

reduce cement expansion.

o Voltage stress [31]:

As mentioned in section 2.1.4, under normal conditions, the voltage

distribution along a string of insulators is not uniform. It is generally

higher at the line end and gets lower towards the earthed end. As a result,

50

the insulator units that are close to the line end experience higher potential

stresses. At high voltages, this leads to the formation of corona around the

cement near the pin and/or between the bottom of the cap and the shell.

Continuous corona discharge on a porcelain insulator will result in

separation of the porcelain shell from the cap and pin in the form of a

‘doughnut’. This phenomenon is shown in Figure 2-17.

Figure 2-17: ‘Doughnut’-like separation of the porcelain shell from its cap and pin (Source: Author’s personal collection)

For glass insulators, corona can lead to the shattering of the glass shell.

Corona gradient rings are normally installed at the line end of an insulator

string of extra high voltage transmission lines to mitigate this problem.

Pollution deposits on insulator shells:

Pollution effluence from the environment which deposits on either glass or

porcelain insulator surface gives rise to leakage current which leads to the

formation of dry-band arcing in high humidity and wet conditions and, eventually,

flashovers [44]. Pollution accumulation also reduces surface resistance, which

effectively reduces the arcing distance of the insulator string. Typical types of

contamination which give rise to this problem include dust of carbon or metal

oxides, soluble salts, bird droppings, and agricultural spray. Because certain

pollution deposits on the insulator discs can be spotted by visual inspection,

utilities normally conduct routine washing of the insulators. Another method to

solve the problem is to apply room temperature vulcanized (RTV) silicone rubber

51

coating on the insulator surface [45] to increase its hydrophobicity which

maintains high surface resistance. Some utilities use creepage or leakage distance

extenders which are attached to the insulators using special adhesives [31].

Internal puncture of porcelain cap and pin insulators [31]:

Lightning strokes with steep wavefronts greater than about 2500kV/µs can

penetrate the corner areas within the cap which is filled by porcelain if there are

defects such as small air pockets or sand band material presence in the areas.

Effectively, the puncture creates a path for the lightning current to conduct

thereby internally ‘shorting’ the insulator. When this happens, the insulator then

loses its insulating property.

For polymeric insulators, the electrical failure modes are due to:

Surface damage

The sheds and rod covering of the polymeric insulator is made up of the same

material. The material not only gives protection to the dielectric FRP rod but also

provides a hydrophobic surface (ability to prevent water filming on the surface) to

the insulator. Certain polymeric materials are prone to damage which can result

from [31]:

o degradation due to effects of solar ultraviolet (UV) radiation which leads

to chalking, cracking and crazing of the insulator surface

o tracking and erosion of the polymeric material due to improperly mixed

and incorrect amount of fillers

o cuts and splices on the polymeric cover and sheds due to corona activity

o attack by birds which like to chew the rubbery polymeric material [7]

Degradation, tracking and erosion of the polymeric material can result in the

insulator losing its hydrophobic properties. When this happens, wetting of

contamination deposits on the insulator can initiate dry band arcing [31] which

can lead to flashover across the insulator. Prolonged corona activity, particularly at

the line end of the insulator, can cause radial splitting of sheds and rod cover

which can also lead to tracking and flashover. As for the bird attack, the reduced

52

number of sheds results in reduced creepage distance which can also result in

flashover.

Flash under [36]

Flash under is the term given to the phenomenon the FRP rod fails due to

moisture ingress. A conductive path is essentially formed within the FRP rod as a

result of the moisture ingress which leads to tracking and eventually flashover

within the rod. The power arc developed within the rod is usually strong enough

to effect a puncture through the polymeric covering, sometimes, fracturing the

rod altogether.

Pollution accumulation

The hydrophobic (water repellence) property of polymeric material makes it

beneficial to use the polymeric insulator in contaminated environments. This is

because water will not accumulate on the sheds and rod cover and thus the

insulator remains dry, even if there is pollution effluence accumulated on the

cover. This prevents dry-band arcing from occurring. However, the insulator can

lose hydrophobicity with age or under extreme polluted and wet conditions [31].

When this happens, dry-band arcing can occur and this can lead to eventual

flashover along the external part of the insulator.

2.5.2.3 Audible noise (AN) and radio interference (RI)

It needs to be acknowledged that, apart from conductors, insulators are also the source

for audible noise (AN) and radio interference (RI) on a transmission line. AN is generated

by electrical discharges and microsparks due to corona activity. It can be detected by the

sounds of crackling noise when standing near transmission lines. RI, on the other hand, is

electromagnetic wave radiation from the conductors as a result of high-frequency current

injection due to discharges on insulators [46]. Both AN and RI levels are higher during

wet weather due to the higher discharge activities. Pollution accumulation on the insulator

surface may also give rise to the noise levels. While there are no failures to the component

of transmission lines which are attributed to AN or RI, the operation of the insulator is

deemed failed if it emits AN and RI beyond the approved standard. Table 2-3 below gives

53

typical requirements for AN inception level and RI limits for insulators based on system

voltage [46]:

System

Voltage (KV)

RI (KV) AN Inception level (DB)

Single unit String Single unit String

132 16 110 24 12

275 26 240 32 18

400 30 320 34 22

Table 2-3: AN and RI limits for insulator at typical system voltages(Source: [46])

Mitigation steps normally taken to solve this problem includes using corona gradient ring

to distribute the high voltage stresses and introducing a hydrophobic layer on the insulator

surface (such as RTV silicone coating or resistive glazing) so that corona discharge does

not occur.

2.6 Chapter Summary

In this chapter, we have discussed the major components of transmission lines, their

respective functions and corresponding failure modes which are referenced throughout

this thesis. It has firstly covered the functions and failure modes of transmission towers

and structures. Then, it addressed the functions and failure modes of foundations,

followed by conductors and, lastly, insulators. Some mitigation steps that utilities take to

counter these failures have also been discussed. It has been highlighted in this chapter that

most of the faults are electrical, mechanical or structural in nature. It has also been

highlighted in this chapter that the major contributing factor to transmission line failures

is the environmental conditions wherein the transmission line traverses.

Most importantly, this chapter has provided the necessary insight into the failure

mechanisms of transmission lines components. Failure of these components normally

starts with gradual degradation in the form of defects. There are diagnostic methods that

are available for utilities to detect these defects. These methods and techniques are

covered in the following chapter.

54

55

CHAPTER 3: INSPECTION, DIAGNOSIS AND MAINTENANCE OF TRANSMISSION LINE COMPONENTS

3.1 Chapter Overview

Having understood the failure mechanisms of transmission line components from the

previous chapter, in this chapter we examine the available inspection and diagnosis

techniques as reported in various sources of literature. This chapter is organized into two

parts.

The first part of this chapter provides a review of the available methods of diagnosis of

the transmission line components. The methods discussed are either currently put in

practice, in experimental stages or on trial by some utilities. Any shortcomings of these

diagnosis methods are addressed.

In order to appreciate the application of test and diagnosis methods, the second part of

the chapter looks into inspection practices and maintenance strategies as currently

conducted by utilities worldwide. A review of survey reports of inspection and

maintenance practices conducted on utilities worldwide is presented here.

Finally, an account of the most common method of inspection, i.e. visual inspection, is

provided towards the end of this chapter.

3.2 Review of Component Diagnosis Methods

Transmission lines in operation are exposed to continuous mechanical and electrical

stresses as well as aging. Under normal circumstances, damage to transmission line

components due to these factors is initially minor and insignificant. Over the course of

time, it can lead to serious damage which, if not rectified in time, can result in forced

interruptions.

Forced interruptions of transmission lines due to any type of failure are highly undesirable

for a number of reasons. These include:

56

The interruption may result in loss of supply to a large number of consumers or

important consumers

Depending on system connectivity and operations, the interruption may initiate

system-wide collapse of a power system

The interruption may incur costs to the utility company

The interruption may create a bad impression to the management of the utility

company

The interruption may result in the utility company not complying to statutory

regulations

Many utilities conduct diagnostic tests on the components to determine their condition

and to predict their useful life. These tests are either done on the components at site -

sometimes during transmission lines inspection - or in laboratories after the components

have been removed.

3.2.1 Test for Tower Structural Strength

The integrity of transmission towers can be accurately determined mathematically using

finite element analysis. Although the computations can be complex, computer programs

are presently available to model the structure and analyse its behaviour under different

simulated stress points. To compare with the results obtained from the computer

simulation model, some utilities conduct actual in-situ tests on tower structures [47].

These tests are usually done on aged structures to assess its integrity and remaining life

[48]. The tests are particularly useful if the utility needs to decide whether to replace the

tower or refurbish it with new conductors.

In these tests, sample towers are chosen from a group of aged tower structures. Test rigs

are used to connect the different parts of the tower at one end and the other end to a

high-powered bulldozer. The bulldozer then pulls the tower from a certain angle to apply

the forces that the towers are subjected to as simulated in the computer program. The

behaviour of the tower under these stresses and how it eventually fails provide

information for the utility to determine the integrity of the tower. This information is also

57

used by the utility to decide what maintenance strategy to use in order to achieve the

desired reliability. Figure 3-1 shows a sequence photograph of a tower site test.

Figure 3-1: Sequence photograph of a transmission tower collapse during a structural in-situ test (Source: [49])

3.2.2 Diagnostic Tests on Tower Foundations

As discussed in section 2.3.2, one of the failure modes of tower foundations is due to

corrosion of steel-reinforced bar in concrete. Unless the degree of corrosion is at the end

stage which is denoted by spalling of the concrete, the status of steel-reinforced bar

corrosion in concrete cannot be determined by visual checks. One of the methods to

determine the probability of steel-reinforced bar corrosion is by using the half-cell

potential measurement [50].

The half-cell is basically comprised of a piece of metal in a solution of its own ions (e.g.

copper bar in a copper sulphate solution). If this configuration is connected to another

metal in its own solution (e.g. iron in ferrous hydroxide solution), there exists a potential

difference between the two half-cells. This difference of potential results in dc current to

flow between the two metals. If a dc voltmeter is connected between the two metals, a

voltage reading can be obtained. The voltage reading indicates the dissolving of the metal

in its own solution. The more negative the voltage reading on the voltmeter, the more

active the metal is dissolving. Figure 3-2 shows the diagram of a half-cell potential reading

of steel-reinforced bar in concrete.

58

Figure 3-2: Diagram of the half-cell measurement method

In the above diagram, if the steel-reinforced bar is not dissolving internally due to

corrosive reaction, the potential measured is small (typically 0 to -200 mV will register on

the voltmeter). If there is reaction and increasing amounts of steel is dissolving, the

potential moves towards -350 mV. At more negative than -350 mV, the steel bar is

corroding rapidly.

The ASTM C876:1991 (American Society for Testing and Materials) [51] provides a guide

to interpret half cell readings in the field. This is given in the Table 3-1:

Half-cell potential relative to

copper/copper sulphate reference

electrode

Percentage chance of

active corrosion

> -200 mV Low (10% risk)

-200 mV to -350 mV Intermediate (50% risk)

< -350 mV High (90% risk)

< -500 mV Severe corrosion

Table 3-1: ASTM C867:1999 criteria for corrosion of steel in concrete (Source: [51])

Together with visual inspection of the condition of concrete foundation, half-cell

potential measurement provides an indicator of the integrity of the foundation.

Half cell

Concrete

Steel-reinforced bar

Voltmeter + ferrous solution

59

3.2.3 Diagnostic Tests on Conductors

As discussed in section 2.4.2, the major failure modes of conductors are due to corrosion,

vibration and annealing.

For an ACSR conductor, corrosion will commence when there is loss of galvanizing of

the steel conductor. The loss of galvanization can be determined non-destructively by

using the CORMON detector, which utilises an eddy current sensor incorporated into a

motorized trolley that travels along the conductor to determine the level of galvanization

loss [52]. The information is then relayed to a ground-based receiver where the data is

stored for analysis. This method gives a signature of the condition of the conductor/joints

and is quite reliable as the reading is not affected by humidity or ambient conditions.

However, the CORMON equipment is very expensive and is rarely used in utility

practices.

The effect of corrosion on the conductor is to cause loss of surface contact between

strands of the conductor and also within the joints. When current flows through the

conductor, the area where there is loss of contact due to corrosion exhibits high

resistance. This results in high temperature at that particular location. Therefore, the

presence of corrosion along the conductor can be determined by using a thermographic

camera or by measuring its resistance.

Both methods may be used together for increased effectiveness. The live conductor and

joint are firstly scanned using infrared equipment. This can either be done from the

ground or from the air. A good conductor/joint should have a uniform temperature

reading throughout. From the infrared equipment, hotspots can be easily determined by

light/bright areas whereas cold/normal areas exhibit dark regions. Careful interpretation

of the infrared indication is required since measurement can be affected by convective

cooling of the conductor, electrical loads, component emissivity, thermal gradient and

ambient conditions [53]. The user then notes the location of the hotspot and flags it to the

maintenance crew who will then perform resistance measurement at the location.

Current technology makes it possible to make conductor resistance measurement live

from the air [25]. Measurement of the resistance of the conductor at the location will

60

confirm whether it was a true hotspot caused by deteriorating joint/conductor or just a

solar reflection. For example, Marshall [25] explains that in Transpower New Zealand, the

known ACSR ‘Goat’ conductor resistance of 89.1 microohm/m is compared against the

resistance of the joint in question. A good joint should have lower resistances (between

45-65 microohm/m). If the joint or conductor exhibits resistances higher than 89.1

microohm/m, the joint is deemed to need replacing.

Tests for conductor strength are normally done in the laboratories. Samples of conductor

lengths that are taken from a particular site are subjected to tensile and torsional tests.

They are normally subjected to a tensile load of 1% of the maximum strength and the

number of turns it takes for the conductors to break [52] is recorded. The results of the

test provide information about the condition of the conductor and they can also be used

to predict its useful life. Experiments [54] conducted by Harvard et al. indicate that in

general, aged conductors that have experienced prolonged vibration have lower tensile

strength and ductility compared to those conductors that are situated in less aggressive

environments.

3.2.4 Insulator Diagnostic Tests

There are several in-service inspection and diagnostics methods that are being employed

by transmission utilities to determine the condition of insulators:

Visual inspection:

This method is employed by almost all utilities in the world. Although is relatively

easy to implement, the success of visual inspection of the insulators is associated

with the level of detail that can be seen from the observation point and the

acquired experience of the observer. Many utilities use the helicopter for this

purpose. The use of high-powered binoculars is also quite common while doing

the visual inspection. The following conditions can be detected by doing a visual

check of the insulators [31, 36, 55]:

o Surface pollution

o Flashover burn marks

o Chipped/cracked porcelain

61

o Broken glass

o Pin corrosion

o Polymeric shed damage due to gunshot

o Exposed FRP rod

o Chalking, tracking and splitting of polymeric cover and shed

Abnormal observations are normally recorded for further observations,

rectification or corrective actions depending on the severity of the condition.

The ‘Buzz’ Method [31, 56]

This method is mainly done on porcelain insulators to determine whether there is

any internal short. The ‘Buzz’ method works on the principal that when two

metallic parts with different potentials are short-circuited, arcing occurs. This is

done by using an instrument with two metallic prongs at the end of a hot stick.

The test is conducted on live lines whereby each of the insulator units on a string

is tested individually by placing the prongs across each cap. For healthy insulator

units, there is a potential difference across the units that are enough to cause

sparks in the small air-gap at the tip of the prongs. The sparking activity comes

with a buzzing sound - hence it is called the ‘Buzz’ method. No buzzing means no

potential difference and therefore the insulator is deemed defective.

This method is simple, requires little operator experience, relatively inexpensive,

and provides clear cut results. However, it is very time consuming because each

insulator on the tower needs to be tested. The work is particularly laborious at

higher transmission voltages due to the large number of insulators per string.

Resistance Measurement Method [31, 56]

This method uses an instrument which has a DC source for resistance

measurement. The instrument, which has two prongs, is located at one end of a

hot stick and a meter that indicates the DC resistance of the insulator unit at the

other end. The two prongs are inserted across each cap and the meter provides

resistance values that indicate either a defective or good insulator. High DC

resistance readings of up to 5 mega ohms suggest healthy insulators whereas lower

resistance readings indicate faulty ones.

62

This method is simple and provides clear cut results. However, the readings can

be affected by relative humidity as they are only reliable if the measurements are

taken in fairly dry weather conditions. Like the buzz method, this method is

laborious and time consuming as each insulator on a string needs to be tested.

Infrared Thermography

This method is employed on cap and pin as well as polymeric insulators. Infrared

equipment is used from the ground or in the air to scan the temperature

distribution along the insulator string. Healthy insulators on a string exhibit high

temperatures at the pin area whereas defective (punctured) units are cold as they

do not support any voltage. From the thermovision equipment, light/bright areas

denote healthy units whereas dark areas indicate faulty units.

Although the thermovision equipment is expensive, thermographic inspection

allows snapshot evaluation of a string that facilitates fast detection of faulty units

and can prove to be cost-effective for long high voltage lines.

Electric Field Measurement Method [57] [58]

Voltage across an insulator produces an electric field across the insulator surface

and the air surrounding the insulator. For an insulator string, the distribution of

the electric field smoothly varies from its lowest value at the earth end to the

highest value at the live end of the string. If there is a defective insulator unit in

the string, there exists an abrupt change in the magnitude of the electric field and

the field distribution across the string is not smooth anymore. The field

distribution is shown in Figure 3-3 [56]:

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Figure 3-3: Electric field distribution across an insulator string with a punctured unit in the middle (Source: [56])

Measurement of electric field across insulator strings is done by means of an

instrument that is fixed at the end of a hot stick. It has a built-in data logger that

stores electric field reading at each insulator in the string. Starting from the first

insulator unit, the instrument is placed adjacent to it and the user then slides the

instrument along the string all the way to the last unit. The user then slides the

instrument back to the starting point. The data is then downloaded to a computer

which gives a display of the electric field along the insulator string. A graph of the

electric field distribution along the string versus the insulator units provides

information regarding which units in the string are faulty. The graph for an 18-

unit insulator string is shown in Figure 3-4 [56]:

64

Figure 3-4: Variation of electric field along an 18-unit insulator string which shows defective insulator units at insulator number 7, 11, 14 and 15 (Source: [56])

It was found that this method is more reliable in detecting defects in porcelain cap

and pin insulators than the buzz or resistance methods [31]. Attempts have been

made to use this method on polymeric insulators [58] but the test results showed

that ambient humidity and contamination deposits greatly affect readings and

produce confusing results.

Corona Detection Method

Defective insulator units can cause the electric field to be concentrated in and

around the defective units. Under high voltage application, the place which is

experiencing highly concentrated electric field can experience ionic breakdown of

air. The ionic breakdown of air is called corona and its by-products are audible

crackling noise (AN) and radio frequency (RF) interference. A device called the

corona phone [31] which consists of a parabolic dish, very sensitive microphone

and high gain amplifier are used to detect the sound of corona. This device is

normally used to detect corona on polymeric insulators. Once there is evidence of

audible noise, further investigation is done to detect the source of the noise.

However, the measurement can be unreliable as it can be affected by background

noise.

Until quite recently, visual detection of corona with the naked eye was not

possible due to its emission in the ultraviolet range. The development of daytime

65

corona viewing technology allows utilities to observe corona during the day and

indicate the source, position and magnitude of such activity [59]. This special

camera is being used by a number of utilities in the US and is being put into trial

use by utilities in Japan, Hong Kong and the UK. Detection can be done either

from the ground or an aerial platform [60].

This method may prove to be effective due to its straightforward spotting, high

sensitivity and immunity from solar reflection. But there is a problem of

determining whether the corona spotted implies serious damage which requires

immediate fixing. Further investigation still needs to be carried out to determine

the right course of action after corona scanning. The cost of such a camera is still

high and the benefits of the technology have yet to be demonstrated.

Other methods

A review of current literature shows there are other emerging techniques that can

be used to detect in-service faulty insulators. These include:

o Detection by using radio frequency signatures and signal processing

techniques [61]

This technique uses the radio frequency (RF) emission from the faulty

insulator as the input to a system that consists of an antenna, an amplifier,

an oscilloscope and a computer. Radio frequency, which is transmitted in

the frequency range of 30 MHz to 300 MHz, is not affected by

background noise which typically exists in the 10 kHz to 40 kHz range.

The radio frequency pulses are captured by the antenna and stored in the

computer. The data is compared with signature characteristics of good

insulators by using the oscilloscope and digital signal processing

techniques. Faulty insulators can be detected if the captured signal

waveform is different from signal signatures of healthy insulators.

However, further investigation needs to be carried out to locate the faulty

insulators in the string.

66

o Analysis of polymeric insulator samples [62]

This method was introduced by researchers at Queensland University of

Technology. In this method, samples of in-service polymeric insulator

material are taken from energized transmission lines by using a novel

hotline tool. The samples are then analysed in the laboratory by using the

scanning electron microscope and Fourier Transform Infrared (FTIR)

spectroscope to assess their conditions which are represented by

numerical indices of oxidation, chalking and ester/ketone ratio. The

results of the analysis indicate the degradation condition of the insulators

and this can provide information for utilities to take necessary

maintenance actions. However, the practicality of the method has yet to

be proven.

o Detection of corona current pulse [63, 64]

This technique uses corona current pulses that emanates from faulty

insulator units as inputs to a system that consists of a coupling antenna, a

wide-band current sensor and a computer. The system is operated from

the ground and picks up corona current signals from insulators of all the

three phases. Captured signals that are distributed around 20 MHz denote

faulty insulator strings. However, there is still a need to carry out further

inspection to pin-point the faulty insulator units. The method is still being

investigated for its applicability.

o Measurement of Surface Pollution [65]

In this technique, pollution deposits on the insulator surface are used as

an indicator to conduct maintenance. It starts with the collection of

pollution deposits and converting it to equivalent salt deposit density

(ESDD) values. Tests on similar insulators in laboratories have shown that

there exists relationships between leakage current, surface resistance,

flashover voltage and ESDD values in that:

The lower the ESDD value, the lower the leakage current levels

The lower the ESDD value, the higher the surface resistance

The lower the ESDD value, the higher the flashover voltage value

67

The laboratory tests also determine the maximum ESDD values that are

permissible before flashover occurs for each insulator types. The ESDD

values of pollution deposit samples from the site are then used to

compare with the values obtained from the laboratory. Maintenance on

the insulators is only carried out when the ESDD values reach their

critical levels. This method is useful for predicting insulator flashovers

due to pollution but it does not give any indication about the condition of

the insulator itself. In addition, the method can be expensive as the

insulators need to be continuously monitored.

Apart from electrical tests, utilities also conduct mechanical tests to determine the

mechanical strength of the insulator. This is conducted at different intervals to check

whether there is any reduction of mechanical strength compared to the specified

mechanical load of new insulators and to determine mechanical strength limits of

corroded pins for end-of-life criteria.

3.3 Review of Inspection and Maintenance Methods

The main driver for maintenance is to achieve high circuit availability, reliability and safety.

In order to achieve these objectives, transmission lines inspection and maintenance

strategies are generally based on periodic (time-based) maintenance and condition-based

maintenance. Many utilities are considering reliability-centred maintenance (RCM)

techniques to further improve their maintenance quality and reduce maintenance cost.

Periodic (time-based) maintenance is based on inspections that utilities conduct at fixed

intervals. Periodic ground and aerial patrols provide information about the condition of

ROW and access tracks, particularly whether there are any encroachments, human

activities and tree growth. Maintenance actions such as cutting undergrowth and clearing

access tracks are some of the activities that utilities do at fixed periods.

Condition-based maintenance is usually carried out on components such as conductors,

insulators, spacers and fittings so that preventive replacements can be implemented to

68

avoid failures. Increased surveillance is initially carried out on components that exhibit

abnormal conditions and this is followed by sample testing and selective replacement of

the component with similar defect. When it is economically justified, system-wide

replacements of the component are made.

RCM is focussed on maintaining the equipment to achieve the required availability and

reliability whilst optimizing maintenance expenditures [66]. Its methodology is structured

on establishing the required maintenance based on the consequences of failure. Failure of

components that results in the highest cost and economic impact in a rank is given top

inspection and replacement priority.

In order to gain insight into the type of transmission line maintenance practice of utilities

around the world, an overview of two international surveys conducted on utilities

worldwide follows.

3.3.1 McMahon Survey on Inspection Practice of Australian and New Zealand Utilities

In 1995, McMahon conducted a survey [2] of electricity supply utilities operating high

voltage grids throughout Australia and New Zealand. The results of the survey are:

Periodic visual inspections via ground and/or aerial patrols are conducted by all

the utilities in the survey at various intervals. Most authorities conduct ground

and/or aerial patrols twice per year. All authorities in the region maintain records

of defects obtained from line patrols, some using electronic data loggers.

Several authorities use diagnostic techniques such as the CORMON device to

detect conductor corrosion, radiography to detect conductor damage due to

vibration, half-cell potential method to check for grillage corrosion and

thermographic camera to check for hotspots at conductor joints.

Not many utilities carry out efforts to establish end-of-life criteria of the

components and residual strength of ageing structures or components.

69

All authorities have some measure of transmission lines reliability, which is

expressed in terms of availability, outage rates or loss of supply.

Live-line techniques for corrective and preventive maintenance are increasingly

being used by all utilities as system operators have shown a reluctance to take out

important lines. The most common activity in live-line work is changing

insulators, installing aircraft warning devices, installing conductor repair rods, and

removing foreign objects. All live-line works are done using the helicopter.

There is an increasing interest among the authorities towards using computerised

maintenance management systems to assist maintenance planning.

3.3.2 CIGRE Survey of Utility Assessment of Existing Transmission Lines

In 2001, CIGRE formed a task force to collect and compare worldwide information

about practices and experiences on inspection methods, diagnostic tools and defect

assessment of existing transmission lines. The survey was responded by 61 utilities from

30 countries in Africa, Asia, Europe, North America, Oceania and South America. The

findings are incorporated in a report [1]. Some of the findings include:

All of the survey respondents indicate that inspection is limited to visual

inspection. For lines above 150 kV, 74% of the respondents indicate that they use

aerial visual inspection using helicopter. For lines up to 150 kV, 63% of the

respondents point out that visual inspection by climbing towers is their main

practice. Inspection on foot is becoming less popular.

74% of the respondents use formatted checklists for support decision; many of

them use inspection guides that contain different categories (between 2 to 4 levels)

to indicate different defect levels to suggest urgency of repairs. Some utilities do

not use any guides at all.

75% of the respondents point out that the most typical form of defect is

corrosion attack on steel components; many of them categorise corrosion attack

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based on surface extent, location and depth. They also indicate that corrosion

caused by normal weathering is more frequent than by industrial pollution.

The most important cause of tower collapse is structural failure due to wind

loading. This is followed by combined wind and ice loading and ice loading only.

Most utilities take precautions against vandalism and/or terrorism that could

jeopardise the operation of the transmission line. This includes fencing, covering

anchor bolts in concrete, using polymeric insulators and installing anti-climbing

devices.

3.4 Visual Inspection

It has been found from the surveys above that despite the advent of modern instruments

to diagnose defects, a majority of utilities worldwide rely on visual inspection to determine

the condition of transmission line components. Strategic maintenance decisions such as to

replace/repair, flag for next maintenance cycle, or do nothing are all based on information

gathered from visual inspection. Based on the surveys too, utilities conduct visual

inspection either from the ground, from the top of the tower (by climbing), or from the

air (in a helicopter).

3.4.1 Ground-level and Climbing Inspection

In this method, the transmission lines are visually checked by a team of inspectors (also

called linesmen) who walk or drive along the line route – if there is good access. Defects

that can be detected visually during ground level inspection include:

Ground line corrosion of steel tower legs

Cracks on concrete foundations

Corrosion, looseness or arcing marks at earthing joints

Verticality of the tower structure

Missing tower bracings, step bolts, danger plates or tower number/circuit plates

Corrosion of tower bracings

Bent tower bracings

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Looseness of bracing nuts or step bolts

Reduction of clearance between conductor and ground or objects such as trees,

telecommunication lines, low voltage lines, roads, railways and rivers

Ground erosion or earth movements at tower base

Unauthorized man-made structures and/or activities in the ROW

Broken glass insulators or severely chipped porcelain insulators

Missing conductor spacers and/or vibration dampers

Presence of bird nests on tower

Swelling of conductor strands near line terminations

Sometimes it is necessary for the inspectors to climb the transmission tower in order to

get a good view of the components on the upper part of the tower i.e. the cross arms,

insulators, fittings, conductors and earth wires. For instance, the presence of pollution

deposits on insulator surface can be better detected when the inspectors are at the same

level as the insulators. Some utilities conduct climbing inspections after taking the line out

of service while others do it live. For live-line inspections, safety precautions must be

taken into consideration when climbing so that safety clearance limits are not

compromised.

During ground level inspection, the inspectors normally make corrective actions that are

minor in nature such as tightening of loose nuts, cutting danger trees, replacing lost step

bolts and clearing tower base of shrubs. Other defects are normally recorded for further

review and action by the maintenance engineer.

Activities during climbing inspection are limited to observing whether there are any

defects on the components and recording them. Since the component defects are

observed qualitatively, the reports are often described using linguistic terms such as

“medium cracks on porcelain insulator shells”, “heavy rust on tower stubs”, “slight

deflection of tower steel bracings” etc. In many aspects, this is probably the best way to

indicate information about a defect level.

Ground-line and climbing inspection is a very labour intensive, time consuming and, to

some extent, limited method since it very much depends on whether there is access to the

transmission tower. It also incurs high cost as in certain areas the utility needs to use

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special four-wheel-drive vehicles crossing over rugged terrains to get to the towers. For

long lines, it takes a long time to complete a cycle of inspection.

Furthermore, inspectors have to be sufficiently fit to carry out the work and they must

possess a certain level of knowledge to assess the degree of defects. In addition,

experiences have shown that no matter how careful or conscientious the inspection is

performed, there is always a possibility of the inspector missing less pronounced defects

that could lead to eventual failure [3], often resulting in forced interruptions. This may

lead to reduced public confidence in the utility company which, in the end, may affect the

company’s bottom line.

3.4.2 Aerial Inspection

Aerial visual checks are now quite common throughout the world as a means to inspect

the physical condition of transmission lines. The major advantage of airborne inspection is

it provides access to transmission towers which is otherwise impossible to reach using

ground transportation. For this reason too, airborne inspection is also used to conduct

asset mapping and emergency line patrols [67].

Helicopters are normally used to carry inspectors for this purpose as they are able to

hover over the lines, although several utilities in the US use fixed wing aircraft [68]. It

enables location of defects from elevated positions and could provide a better field of

vision to the inspector compared to observing from the ground. It is particularly beneficial

in locating easily observed large scale defects such as broken cross arms, dropped

conductors and severe ground erosion at tower base. Aerial inspection of transmission

lines can be done at a much faster rate compared to ground line inspection. Figure 3-5

shows aerial transmission lines inspection using the helicopter.

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Figure 3-5: Aerial visual inspection of transmission line components using the helicopter (Source: [69])

When conducting the inspection from the helicopter by using still or video cameras, the

observer may be able to locate defects along the conductor span which may otherwise be

impossible to detect with the naked eye. One example is damage of conductor due to

gunshot, shown in Figure 3-6, which was taken from the helicopter:

Figure 3-6: Broken conductor strands due to gunshot (Source: [70])

The disadvantages of airborne visual inspection include [67]:

Observer’s level of alertness may deteriorate over the course of inspection

Noisy environment of the aircraft may affect the observer’s concentration

Rapid rate of passing the structures may affect accuracy of detection

Inspection can only be done in fair and flight-friendly weather

High cost

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Many technological innovations have surfaced in recent years in an effort to make aerial

inspection exercise more effective. This includes automating some of the activities that are

handled by the human observer. A survey [67] was conducted by EPRI to determine the

emerging technologies that are being experimented for use in airborne inspection of

transmission lines. A resume is given in Table 3-2.

Technology Description

Airborne Vibrometry

Inspection Analyzed

by Neural Network

(AVIANN)

Predicts component condition using laser doppler

vibrometer

Robotic Inspection of

Power Lines (RIPL)

Unmanned aerial vehicle which is remote controllable

while in flight and directed to various viewpoints to

identify potential problems

AVCAN Tracker

System

Helicopter mounted cameras complete with GPS and

digital video that capture video photos and temperature

data

MediaMapper Collects digital still images and video that are indexed

with GPS data

Ohmstick Directly contacts energized lines and reads resistance in

micro-ohms of joints, connectors and splices

Ultraprobe Audio recording system used to detect ultrasonic

frequencies emitted from corona discharges

Korona Detection of electric field surrounding transmission lines

and converting it to graphical representations

Table 3-2: Emerging and available aerial inspection technologies

3.4.3 Drawbacks of Visual Inspection

The results of a visual inspection exercise are largely dependent on the information that is

gathered by the inspector. It is therefore important that the inspector provide quality

information so that maintenance planners can confidently use the information to develop

maintenance plans and strategies.

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Unfortunately, inspectors are normal human beings and not machines or robots that can

be programmed to produce accurate results all the time. Defect information that is based

on human visual observation is largely influenced by the inspector’s perceptions. The

interpretation of the defect level which, in this case, can only be described qualitatively, is

successively based on the inherent knowledge, reasoning and experience of the inspector.

One inspector’s interpretation of a certain defect level is different from another. Other

factors that can influence the inspector include the environment wherefrom the

observation is made, the inspector’s health and fitness level, and whether visual aids are

used.

These problems result in a high level of subjectivity and uncertainty during visual

inspection. This often leads to a large variance in defect reporting, which makes it difficult

for the utility management to progressively monitor the condition of the component. As a

result, ineffective or wrong maintenance decisions are taken which extends to ineffective

control of maintenance or repair costs.

Therefore, it is conceivable that a tool is required that can be used by utilities to narrow

down the level of subjectivity and uncertainty associated to visual inspection. Such a tool

must be able to use the linguistic descriptions, infer the condition of the components and

suggest the appropriate maintenance actions. In this way, the interpretation of a certain

defect condition can be standardized and objective decisions can be made to improve

maintenance quality.

3.5 Chapter Summary

This chapter has provided an insight to the various methods of inspection and diagnostics

of transmission line components that have been reported in various literatures. Some of

these methods have been adopted by utilities whereas others are in the experimental

stages. The advantages and disadvantages of the different methods are identified and

discussed.

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Two of the current survey reports on utility inspection and maintenance practice are

discussed. It was found from the surveys reports that visual inspection is by far the main

means of locating defects performed by many utilities in the world.

Finally, shortcomings associated with visual inspection practice of transmission line

components are further commented on.

The next chapter introduces an artificial intelligence technique that can be used to

improve the reliability of visual inspection. The proposed technique utilizes principles of

fuzzy logic.

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CHAPTER 4: FUZZY LOGIC AND FUZZY INFERENCE SYSTEM

4.1 Chapter Overview

It was indicated in the previous chapter that there are inherent uncertainties in the data

collected by visual inspection of transmission lines which are due in part to the inspectors’

limited knowledge. Hence a tool is needed to reduce these underlying uncertainties

associated with visual inspection of transmission line components. Such a tool should be

able to process information based on natural language descriptions which is used by

inspectors when making defect assessments.

Fuzzy logic was introduced in the 1960’s to deal with such fuzziness of human perception

and decision making. The application of fuzzy logic in real world utilization is represented

in knowledge-based fuzzy inference systems.

The following chapter introduces the fundamental principles of fuzzy logic and fuzzy

inference system which the research discussed in this thesis is applied. First, the

fundamental concept of fuzzy logic is elucidated by describing a simple example.

Membership functions and fuzzy rules within the context of their use in fuzzy logic are

then explained. The chapter next proceeds to explain the application of fuzzy logic in a

fuzzy inference system. The processing steps and techniques of inferencing used in a

fuzzy inference system are further deliberated.

Steps taken to design a fuzzy inference system are then described. Some examples of

application of fuzzy logic and fuzzy inference systems in power utility are discussed at the

end of this chapter.

4.2 Fuzzy Logic

The concept of fuzzy logic was developed by Lotfi Zadeh, a professor at University of

California, Berkeley, in the mid 1960’s as a way of processing data based on linguistic

descriptions [4]. Unlike Boolean logic or classical logic, which assumes that every fact is

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either entirely true or false, fuzzy logic extends Boolean logic to handle vague and

imprecise expressions. According to Zadeh [4], the essential characteristics of fuzzy logic

are:

Exact reasoning is viewed as a limiting case of approximate reasoning

Everything is a matter of degree

Any logic system can be fuzzified

Knowledge is interpreted as a collection of equivalent and fuzzy constraints on a

collection of variables

Inference is viewed as a process of propagation of fuzzy constraints

The idea is best explained by using an example. Suppose that Boolean logic is used to

identify whether a room temperature is “hot” or “cold”. Most people would agree that

40oC is a “hot” room temperature and 10oC is a “cold” room temperature. However, if

the room temperature falls to 25oC, it becomes much harder to classify the temperature as

“hot” or “cold”.

The concept in reality allows imprecision to be expressed in a quantitative fashion. This is

done by introducing a set membership function, represented by μA(x), which maps

element x to real values between 0 and 1; the value indicates the degree to which an

element belongs to set A. A membership value of 0 (μA(x) = 0) indicates the element x is

entirely outside the set, whereas a μA(x) = 1 indicates the element x lies entirely inside the

given set A.

Consider then the previous example: if fuzzy logic is used to represent the “hotness” of a

room, 40 oC would have a membership value of 1 and 10 oC would have a membership

value of 0. 25 oC, on the other hand, would have a “hotness” membership value of, say,

0.6 and a “coldness” membership value of, say, 0.3.

4.2.1 Membership Functions

Membership values can be expressed in different types of fuzzy membership functions.

The most common representations are the triangular and the trapezoidal membership

functions [71]. Figure 4-1 shows an example of a triangular membership function, which

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is expressed by (a1, a2, a3); it should be noted that the closer the number is to 20, the

higher the degree of membership to number 20:

Figure 4-1: Triangular membership function (10, 20, 30)

To account for all values of the numbers between 10 and 30, the membership function

above can be represented mathematically by (Eq. 4-1):

30xfor,0

30x20for,10/x30

20x10for,10/10x

10xfor,0

(x)μ A (Eq. 4-1)

The membership function in Figure 4-1 can also be represented by a technique called the

resolution identity of membership functions [72]. The technique is based on the

decomposition of the membership function into non-fuzzy level-sets or intervals by

slicing the membership function at different membership levels (also called alpha-cuts or

α-cuts) between 0 and 1. An α-cut set, denoted by Aα, consists of all elements that have a

membership grade in the membership function greater than or equal to the value of α

[72]. This definition can be written as:

α(x)μXxA Aα (Eq. 4-2)

Degree of membership, μA

1

0

10 20 30

Number (x)

0.5

A

0.75

0.25

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Therefore, the membership function A can be represented by the summation of all its α-

cut sets:

1

0AA (Eq. 4-3)

Thus the membership function shown in Figure 4-1 can also be represented by the values

in Table 4-1 below:

Α-CUT Lower boundary Upper boundary

0 10 30

0.25 12.5 27.5

0.5 15 25

0.75 17.5 22.5

1 20 20

Table 4-1: Determination of membership function from α-cut sets

Other membership functions that are in use include Gaussian, Bell, and Sigmoid [73].

The intersection and union of 2 membership functions, A and B, are represented by (Eq.

4-4) and (Eq. 4-5) and Figures 4-2 and 4-3 respectively:

Intersection (AND operator): )(),(min))(( xBxAxBA (Eq. 4-4)

Figure 4-2: Intersection of 2 membership functions (A AND B)

A B

A ∩ B

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Union (OR operator): )(),(max)( xBxABA (Eq. 4-5)

Figure 4-3: Union of 2 membership functions (A OR B)

4.2.2 Fuzzy IF-THEN Rules

The application of fuzzy logic in real world systems is mainly used with fuzzy IF-THEN

rules. In this application, conditional statements take the form of:

IF < premise > THEN < consequence >

whereby both premise and consequence are characterized by fuzzy or linguistic elements

respectively. Due to their straightforward forms, fuzzy if-then rules are often employed to

process information captured by human reasoning in order to make decisions that are

based on linguistic inputs.

An example that illustrates this relationship is:

IF < food is excellent > THEN < tip is high >

where ‘excellent’ and ‘high’ are linguistic variables that can be characterized by

membership functions.

Through the use of linguistic inputs and membership functions, fuzzy IF-THEN rules can

easily capture the spirit of “rule of thumb” used by humans. This is done by incorporating

them in fuzzy inference systems.

A B

A U B

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4.3 Fuzzy Inference System

Fuzzy inference systems [71] are essentially knowledge-based systems that utilize all of the

concepts that have been described in the previous sections: fuzzy logic, fuzzy IF-THEN

rules and membership functions. Figure 4-4 shows the components of a fuzzy inference

system:

Figure 4-4: Components of a fuzzy inference system

As shown in the above figure, a fuzzy inference system is made up of five functional

blocks. They are:

A rule base containing a number of fuzzy IF-THEN rules

A database which defines the membership functions

An aggregator which performs the inference operation based on the rules

A fuzzification interface which transforms the crisp inputs into degrees of match

with linguistic values

A defuzzification interface which converts the fuzzy results into crisp outputs.

The steps performed by fuzzy inference systems when processing inputs are:

Fuzzification interface

Knowledge base

input output

Database Rule base

Defuzzification interface

Aggregator

Fuzzy Inference system

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1. compare the input variables with the membership functions of the premise part to

obtain the membership values of each linguistic terms – this step is called

fuzzification

2. combine the membership values of the premise part to deduce firing strength of

each rule using the selected operator

3. generate the consequence or results of each rule

4. aggregate the results or consequences to produce a crisp output – this step is

called defuzzification

4.3.1 Inference and Defuzzification Techniques

There are a number of inference techniques used in a fuzzy inference system. One of the

techniques that is used mostly in various practical applications is the Mamdani inference

technique [73].

The Mamdani technique was introduced by Professor Ebrahim Mamdani of London

University in 1975 to control kiln temperature in a cement factory [71]. To come up with

the technique, he applied a set of fuzzy rules supplied by experienced human operators

rather than using theoretical formula. The technique takes the form of:

IF < x is A > AND < y is B >

THEN < z is C >

where A, B, are fuzzy sets in the premise and C is a fuzzy set in the consequence.

Figure 4-5 illustrates the Mamdani inference technique for a 2-input 1-output system.

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Figure 4-5: Mamdani fuzzy inference method (Source: [74])

In the above figure, A1 and A2 represent the degree of memberships for fuzzy input x and

B1 and B2 represent the degree of memberships for fuzzy input y. Use of the min operator

indicates that the minimum of these two values are mapped to fuzzy output z, indicated

by areas under C’1 and C’2 respectively. The resultant, which is the area under C’, is an

aggregation (union) of areas under C1’ and C2’. In order to indicate an appropriate

representative value for the final output, the aggregated output membership function has

to be converted into a crisp form. The conversion is done in the defuzzification step of

the inference system and can be achieved using any one of the five computational

schemes:

Centroid or Centre-of-Area (COA), zCOA:

This method calculates the point which is central to the area under the aggregated

output membership function. It is calculated using the following equation

dzz

zdzzz

A

A

COA)(

)(

(Eq. 4-6)

where μA(z) is the aggregated output membership function. This is the most

commonly used defuzzification technique and is very accurate. The only

disadvantage of this technique is that it can be computationally difficult for

complex membership functions.

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Bisector of Area (BOA), zBOA:

This method calculates the area under the aggregated output membership

function and divides it into two equal areas. The point of division is returned as

the bisector of the area. It is calculated using the following equation:

BOA

BOA

z

a

b

z

AA dzzdzz )()( (Eq. 4-7)

where ZzzbZzza max,min and zBOA is the value at which the area

is divided equally by 2. This technique can also be computationally difficult if the

out membership functions are complex.

Mean-of-Maximum (MOM), zMOM:

This method calculates the arithmetic mean of all the maximum values of the

aggregated output membership function. It is calculated using the following

equation:

dz

zdzzMOM (Eq. 4-8)

Smallest-of-Maximum (SOM), zSOM:

This method returns the smallest of the maximum values on the aggregated

output membership function. This defuzzification technique is very fast as it

doesn’t require any calculations.

Largest-of-Maximum (LOM), zLOM:

This method returns the largest of the maximum values on the aggregated output

membership function. For the same reason as the Smallest-of-Maximum

defuzzification technique, this technique is also very fast.

The above methods are graphically represented in Figure 4-6:

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Figure 4-6: Defuzzification schemes to derive a crisp output (Source: [74])

4.3.2 Developing Fuzzy Inference Systems

There are essentially 5 steps that one can follow in developing fuzzy inference models

[71]:

1. Specify the problem and define linguistic variables used

2. Determine fuzzy membership functions

Membership functions can take a variety of shapes. However, trapezoidal and

triangular membership functions can often provide adequate representation of the

expert knowledge, while at the same time, simplifies the process of computation

3. Elicit and construct fuzzy rules

At this point, the expert might be asked to describe how the problem can be

solved using the fuzzy linguistic variables defined previously. The required

knowledge can also be obtained from other sources such as books, manuals,

specifications or observed human behaviour.

4. Encode the fuzzy membership functions, fuzzy rules and procedures to perform

fuzzy inference in an expert system

This can be done in either one of two methods: to write the codes and algorithms

using high-end computer programming languages such as C/C++, FORTRAN or

PASCAL, or to use fuzzy logic development tools such as MATLAB Fuzzy Logic

Toolbox or other commercially available fuzzy knowledge builder software.

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5. Evaluate and tune the system

Finally, the system is tested to check whether it conforms to the required settings

specified in the beginning. This step may prove to be laborious as various levels of

input data needs to be fed into the system and the outputs verified to the

satisfaction of the expert. Tuning the system may be required and this can be done

by:

Reviewing the model input and output variables

Reviewing the membership functions. If necessary, additional

membership functions may be required to be defined.

Providing sufficient overlaps between neighbouring membership

functions. It is suggested in [71] that triangle-to-triangle and

trapezoidal-to-triangle membership functions should overlap around

25% to 50% of their bases.

Reviewing the rules. If necessary, additional rules may be required to

be defined.

Revising the shapes of the membership functions.

4.4 Applications of Fuzzy Logic in Utility Environment

Several published references have shown the benefits and successful application of fuzzy

logic in the utility environment. These applications provide the motivation to implement

fuzzy logic in this research. Below is a resume of some of these works:

Kumar et. al. [75] designed a fuzzy inference system to determine the type of

faults on a transmission line. The inputs to the fuzzy inference system are times

required for the relay to trip (“very small” to “very large”) and the characteristic

impedances of the line (“low”, “medium” and ‘high”). The output variables for

the system are estimated distance of fault (“very close” to “very far”) and the type

of fault (phase-phase fault, phase-ground fault and three-phase fault). Inference is

by means of IF-THEN rules that relate the input membership functions and the

out membership functions.

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The system shows that linguistic representations of a range of crisp values (such as

relay operating time and line impedances) can be used to infer the type of fault on

the line. Further action can be taken once the fault is identified by the inference

system.

Hathout [76] introduced a method which he termed as “soft reliability

assessment” to infer the reliability of transmission steel structures taking into

consideration the structural design safety factors and damage consequences of

deteriorating transmission steel structures. The level of deterioration is gathered

from field inspection of structures and is represented by linguistic terms such as

“very poor”, “fair” and “very good”. He used complex mathematical

manipulations to relate the structure’s failure probability with cumulative

distribution function of its standardized safety margin and its coefficient of

variation of safety margin. Together with the fuzzy indicators of structural

deterioration, he then used a computer program to determine the remaining

structural integrity. Depending on the output of the program, further steps can be

taken to improve the reliability of the structure.

Hathout’s method shows that fuzzy logic principles need not be used in a fuzzy

inference system to process information. The method shown is rather

complicated but the use of computer program greatly simplifies calculation.

Marannino et. al. [77] developed a rule-based fuzzy logic decision support system

to mitigate the risks of a power system voltage collapse. The input to the support

system is a set of numerical variables that represent a snapshot of the operating

system. This input is converted to symbolic and linguistic quantities (“low”,

“average” and “high”) which are processed by a set of IF-THEN rules in the

fuzzy logic system. The rules were devised by human experts. The output of the

system provides a measure of the security level degradation of the power system

with respect to the voltage collapse risk (“stable network”, “stressed network” or

“collapse”). The system was successfully tested on the Italian extra high voltage

network.

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The system shows that the rules used in the inference system can be designed to

model the reasoning of human expertise. These rules are used in an inference

system that helps power network control engineers manage risks of voltage

collapse.

Islam and Ledwich [78] used fuzzy logic principles to relate results of transformer

dissolved gas analysis (DGA) to types of incipient faults within the transformer.

Input data of different levels of dissolved gases in transformer oil such as

hydrogen, ethylene, methane and ethane, were converted to linguistic variables

(“low”, “medium” and “high”) and different types of incipient faults (such as

“thermal fault of high temperature”, “partial discharge of low energy intensity”

and “electrical discharges of high energy”) were inferred using a fuzzy logic

system. The authors used the semi-Cauchy type of membership function shape

which “provides smooth transition of overlapping ascending and descending

membership functions”. Tests on three power transformers showed that the

system was capable of providing good results.

The applications mentioned above show that fuzzy logic is suitable for use in a decision

support tool to solve power system problems. The use of appropriate membership

functions and IF-THEN rules that represent expert knowledge in a fuzzy inference

system allows information based on natural language be processed to suggest an action.

These principles are used to design a knowledge-based fuzzy inference system for the

assessment of transmission lines components. In the next chapter, the design and

application of a fuzzy inference system to determine the condition of one of the most

common and important components of transmission lines – porcelain cap and pin

insulators – is shown.

4.5 Chapter Summary

This chapter has described the principles of fuzzy logic and its application in fuzzy

inference systems which are used in this research. The basic components of a fuzzy

inference system i.e. membership functions, IF-THEN rules and defuzzification

techniques have also been described. Some example applications of fuzzy logic and fuzzy

inference system in use at power utilities have been presented. However, this is not an

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inexhaustible list of sample usages of fuzzy logic in power utilities as there are various

other applications. The reader should be led by the bibliography for further reading on the

subject.

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CHAPTER 5: USING FUZZY LOGIC FOR TRANSMISSION LINE DEFECT ASSESSMENT

5.1 Chapter Overview

In the preceding chapters, we have reviewed the failure modes of transmission line

components and the methods that are available to detect component defects. From the

review, we have found that most utilities still use visual inspection as the main method of

detecting component defects. As highlighted towards the end of chapter 3, visual

inspection is a process that is inherently fuzzy. We therefore would like to examine

whether fuzzy logic techniques can be applied to the improvement of transmission line

visual inspection.

The components that are normally inspected during routine transmission line visual

inspection include towers/structures, conductors, fittings and insulators. In this chapter,

we primarily present the design and implementation of a knowledge-based fuzzy inference

system (FIS) applied to the visual inspection of porcelain cap and pin insulators.

Firstly, a closer look at the mechanisms for insulator pin corrosion deterioration and

broken porcelain shells are presented. Then, the chapter proceeds to explain the design

and development of the insulator inspection fuzzy inference system. Reference is made in

the chapter to the inspection practice of two utilities - Tenaga Nasional Berhad (Malaysia)

and Powerlink Queensland (Australia) – to illustrate current utility practice in locating

insulator defects. The fuzzy inference system is then applied to actual insulator data from

both the utilities to simulate the use of the system during an insulator inspection exercise.

Discussions regarding the experience of using the insulator inspection FIS are also

presented.

We then proceed to show that the output of the insulator inspection FIS can be used to

assess the condition of a string of insulators and further, multiple strings in a transmission

line. With the insulator string condition rating and a proposed numerical rating for

severity of environment, a mathematical model is then developed to estimate the

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recommended year for bulk replacement of corroded insulators. Simulated insulator data

for different types of environment are used to show the applicability of the model.

The chapter further explains how the FIS can be applied to the inspection of other

components of the transmission line.

Finally, an account of the potential savings in inspection and maintenance expenditure

that can be achieved due to the introduction of FIS in Tenaga Nasional Berhad is

deliberated at the end of the chapter.

5.2 Application of the FIS on Porcelain Cap and Pin Insulators

Electric utilities around the world are currently using millions of porcelain cap and pin

insulators. The fact that these insulators are still being used, despite the advent of

‘modern’ insulators such as those made of polymers, show that utilities have traditionally

been satisfied with the performance of porcelain cap and pin insulators. In many utilities,

however, these insulators are either operated close to or beyond their intended service life

(30 to 50 years), many of which have recently resulted in an increase of observed

problems [55].

As discussed in Section 3.2.4, the main method of in-service porcelain cap and pin

insulator assessment is by visual assessment. The reason for this is because the type of

defects associated with porcelain cap and pin insulators can be detected by assessing their

physical appearance. The two major types of defects identified in this thesis are corrosion

of the pin and breakage of the porcelain shell.

In doing the insulator assessment to determine these types of defects, the inspector makes

a visual observation either from the ground (often using visual aids such as high-powered

binoculars or cameras), from the top of the tower (by climbing the tower structure) or

from the air (in helicopters). Maintenance actions such as ‘do nothing’, ‘flag for next

maintenance’ or ‘replace immediately’ are often taken as a result of these observations.

Thus, it is imperative that the assessment exercise returns reliable report regarding the

condition of the insulators so that the correct maintenance action is taken.

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However, as discussed in Section 3.4.3, there exist the inherent problems associated with

human visual inspection. One is that the observer makes his own judgment with regards

to how he interprets the level of defect. As a result, defect reports from visual inspections

vary from one observer to another. Another problem is that descriptions of the defects

are usually explained by qualitative indicators that use linguistic terms. For example, the

different levels of rusty insulator pins are best represented by statements such as “light

rust”, “medium rust” or “very rusty”. Such statements are vague and subjective. As a

result, in time, it is difficult to monitor insulator condition and to decide the appropriate

maintenance action. Therefore, it is conceivable that a tool or method is made available to

address these inherent problems.

5.2.1 Factors Affecting Pin Corrosion

Insulator pin corrosion is primarily caused by the electrolytic action of leakage current.

Leakage current is dependent on the voltage stress and the level of contamination. Other

factors that affect the rate of corrosion are:

Thickness of the pin

Amount of galvanising of the pin

Orientation of the insulator string (suspension or tension)

Rainfall

Presence of zinc sleeve

If we assume that the voltage profile along an insulator string is linear, then the voltage

across each insulator unit on a 10 unit string at 132 kV would be around 7.5-8 kV and on

a 16 unit string at 275 kV about 10 kV. But because of strong capacitance, the voltage

profile along insulator string is not linear and typically takes the form shown in Figure 5-1

[7].

94

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10

Insulator Units (Earth end to line end)

% L

ine

volt

age

Figure 5-1: Voltage profile across a 132 kV insulator string [7]

The above figure shows that the highest voltage stress is at the line end where up to 20%

of the line-to-ground voltage can apply. A high voltage stress increases the likelihood of

electrical discharges. The stress is even higher if the insulators are contaminated, and with

moisture present, corona activity can arise. One of the by-products of corona is ozone

which can accelerate the corrosion process [29]. This explains why in some areas,

corrosion is prevalent at the last insulator in the string. This is also a reason why some

utilities give particular attention to the insulator unit at the line end of the string.

Corrosion is more likely to occur in areas of high pollution, particularly where there is salt

spray from the sea and very few rainfall washings. The rate of corrosion attack is also

higher in these areas due to electrolytic reaction between salt deposits and atmospheric

humidity.

A CIGRE survey [79] on the serviceable life of line components found a range from

around 25 to 80 years with a mean of 50 to 60 years for porcelain cap and pin insulators.

Good quality porcelain insulators in clean environments are therefore expected to give an

average serviceable life of 50 years.

Insulators used in TNB for the 132 kV and 275 kV transmission lines have pin sizes of 16

mm (70 kN SML) and 20 mm (120 kN SML) respectively. The size of the pin will affect

the leakage current density and hence the rate of corrosion. Most of the pin corrosion

problems in TNB occur on the 132 kV towers.

95

The orientation of the insulator string on the tower, either in suspension or tension,

affects the rate of corrosion. Most corrosion problems occur on suspension strings. This

is because the undersides of the insulators do not get washed properly with rain and

pollution level can build up in that location. Furthermore, moisture does not escape easily

on the undersides of suspension insulators and this leads to an increase in corrosion rates

in areas of high rainfall and humidity.

To decrease the rate of pin corrosion, TNB uses insulators with zinc sleeves around the

pin. These sleeves are specified to be not less than 3 mm and its purity must not be less

than 99.9% in order to provide good galvanic performance. The zinc sleeve is fused on

the pin shank to cover the area around the cement line of the insulator. It prolongs the

rate of corrosion by acting as a sacrificial element before being fully consumed to expose

the steel pin.

The failure mechanism of insulator pins due to corrosion is as follows:

The galvanising layer corrodes and is lost, exposing the steel pin

Corrosion of steel occurs, reducing the pin diameter and increasing tensile stress

in the pin

The load stress exceeds the ultimate tensile strength of the pin, causing the pin to

fail, and the conductor is dropped.

Based on this failure mechanism, several levels of corrosion can be detected visually to

indicate the approximate criteria for insulator pin failure. Four distinct categories of pin

corrosion levels are shown in Figures 5-2, 5-3, 5-4 and 5-5 below:

Category 1 (No rust):

Figure 5-2: No rust insulator pin condition

96

Category 2 (Light Rust):

Figure 5-3: Light rust insulator pin condition

Category 3 (Medium Rust):

Figure 5-4: Medium rust insulator pin condition

Category 4 (Heavy Rust):

Figure 5-5: Heavy rust insulator pin condition

97

The insulators shown in Figures 5-2 to 5-5 have no zinc sleeve around the pin and in

Figure 5-3, most of the pin galvanising layer has been lost.

Moubray explains that the term “potential failure” is the failure process at which it is

possible to detect that the failure is occurring or about to occur [66]. The rust conditions

of the pin can indicate its potential to fail i.e. break. And because rust conditions on an

insulator pin deteriorate with time, the relationship between the pin’s rust condition and

the time the insulator is put in service can be represented by the P-F curve; the P being

the point in time when we can determine that the pin is failing (“potential failure”) and the

F is the point at which it fails. This curve is shown in Figure 5-6.

Figure 5-6: Schematic representation of P-F curve for rust condition of insulator pin

The P-F curve shown above has a big influence on the insulator inspection guide that is

used in the field. This is because data from the field inspection should reflect the actual

condition of the insulator and the state it is in with respect to the P-F curve. The most

critical point is the state of the insulator at point P on the curve.

Point P is an indication of the strength of the pin at which its remaining mechanical

strength may not be enough to support the weight of the conductor and other insulators,

particularly when there is contribution from wind and/or snow. It is critical that the

inspector be able to detect the 'point P' condition of the insulator pin from the guide so

Rust Condition

No rust

Failure

Commission End of life

P-F Interval: Remaining time before imminent failure

Light Rust

Medium Rust

Heavy Rust

P

F Year

98

that appropriate action can be taken before it reaches a failure state. Some of these actions

include to increase inspection intervals or to replace the insulator.

It does not matter where on the string the insulator unit with rusty pin is as it only

requires one really bad pin to take the whole line down. Therefore, for pin corrosion

failure mode, the insulator unit with the worst pin corrosion in the string should be

detected as an indicator of the remaining mechanical strength of the string.

5.2.2 Factors Affecting Chipped/Broken Porcelain Insulator Disc

The porcelain disc is the part of the insulator that is largely responsible for its electrical

performance. It provides the creepage distance that resists flashover that is due to either

power frequency, switching surge or lightning surge. Consequently, insulators are designed

to withstand high levels of electrical stress.

There are two types of cap and pin insulators normally manufactured for transmission line

application: the normal type and the anti-fog type. The difference between these two types

is determined by the length of their creepage distance. Selection of the types is determined

by the environmental condition of the areas through which the transmission line passes.

Commonly-used insulators typically have creepage distance of around 300 mm. They are

used on transmission lines that operate in clean and moderate environments. Figure 5-7

shows a cutaway drawing of this type of insulator showing the orientation of the ribs

underneath the insulator:

Figure 5-7: Cutaway drawing of a normal type cap and pin insulator (Source: [80])

99

The anti-fog type insulators have longer creepage distance, around 440 mm, and are used

in areas where the pollution levels are high. Figure 5-8 shows a cutaway drawing of an

anti-fog type insulator showing the extended ribs underneath the insulator:

Figure 5-8: Cutaway drawing of an anti-fog type cap and pin insulator (Source: [81])

Electrical failures on porcelain insulators can occur internally or externally. Internal

punctures are due to defects in the porcelain material in the form of voids and impurities.

These defects result in electrical stress concentrations within the porcelain that could lead

to the formation of an electrical path. An internally shorted insulator cannot be identified

by visual inspection.

External electrical failures occur in the form of flashovers. It is the dielectric breakdown

of air as a result of very high electrical stress. On a string, the arc produced by the

flashover usually travels away from the porcelain surface. External flashovers are

determined by the magnitude and duration of the electrical stress and the condition of the

insulator discs [31].

The number of insulators in a string provides the required insulation for the string to

withstand flashover. Generally, the higher the line voltage, the longer the insulator string

is. If the number of insulators in a string is reduced, this withstand distance will be

compromised and therefore flashover is likely to occur.

As discussed in Section 5.3, the voltage stress is highest at the line end and reduces in a

non-linear fashion as we approach the earth end. Tests conducted by Booker and Jagtiani

[82] on insulator strings found out that the performance of the insulators at the line end

degrade more rapidly than those at the earth end due to the effect of higher local

temperature. They concluded that the primary reason for the higher temperature is due to

100

the magnitude of electrical stresses at the line end. Therefore, it is important that the

insulator units at the line end of the insulator string are in good condition to withstand the

higher voltage stress.

Porcelain is a very dense and tough material. It does not shatter like toughened glass

insulators do when hit by an object such as gunshot bullets. Depending on the force of

the impact, the porcelain either suffers cracks on the surface, chips, or breaks. Cracked

surface, chipped or small-sized fractures do not affect the mechanical and electrical

properties of the insulator and the overall integrity of the insulator string. But total

breakage of the porcelain disc may pose a problem as it would reduce the creepage

distance of the insulator string. If the insulator concerned is located at the line-end of the

string where the voltage stress is the highest, then flashover failure may be imminent.

Tests on damaged insulator discs have also found that the residual mechanical strength of

the insulators is reduced by up to 35% [29]. Several levels of porcelain disc breakage are

shown in Figures 5-9, 5-10, 5-11 and 5-12:

Figure 5-9: Chipped porcelain disc (Source: Author’s personal collection)

Figure 5-10: Small breakage of porcelain disc (Source: Author’s personal collection)

101

Figure 5-11: Major radial breakage of porcelain disc (Source: Author’s personal collection)

Figure 5-12: Total porcelain disc breakage (Source: Author’s personal collection)

The outer physical condition of the porcelain discs on a string can be determined fairly

easily during insulator visual inspection. Figures 5-13 and 5-14 show an insulator string in

service which suffers broken discs as perceived during visual inspection of transmission

lines.

Figure 5-13: Arrows show partially broken porcelain discs in an insulator string (Source: Author’s personal collection)

102

Figure 5-14: Arrows show 2 totally broken porcelain discs in an insulator string (Source: Author’s personal collection)

The levels of defect on porcelain discs can then be categorised as shown in Table 5-1:

Category Condition

1 – No breakage No observable defects on porcelain disc

2 – Chipped Porcelain disc has suffered observable chips at the outer

edge which do not extend into the second rib

underneath the insulator. The insulator still retains its

circular shape.

3 – Small breakage Porcelain is observed to be broken up to the second rib

underneath the insulator. The insulator is observed to be

no more round in shape.

4 – Large breakage The broken porcelain is extended to the head of the

insulator. At least half of the insulator disc is gone.

5 – Total breakage The porcelain disc does not exist.

Table 5-1: Category of in-service porcelain insulator disc defects

Defects on porcelain discs generally do not deteriorate with time. The defects are random

and mostly are due to public vandalism from rifle fire or slingshots.

103

5.2.3 The Insulator Visual Inspection Process

Because pin corrosion and broken porcelain disc can be physically located, utilities

normally use visual inspection to detect these defects. To illustrate, the practice of two

utilities is given here as examples:

TNB Practice:

In TNB, inspection of insulators on 132 kV and 275 kV transmission lines is done

during routine 3-yearly planned maintenance outage of the line. During the

inspection, the inspector climbs up the tower and records the condition of the

insulator in a form which is part of an inspection report. There is no inspection

guide used and the condition of the insulator is determined by the sole judgment

of the inspector. The format of the inspection report is in the form of a tick (√)

denoting the insulator is in good condition and a cross (X) denoting it is in bad

condition. Intermediate insulator conditions are taken note of and written in a

separate table in the report form. The lines maintenance engineer then uses the

information in the report to decide whether to take no action (for good condition

insulators), flag for next maintenance (for partially defective insulators) or replace

immediately (for bad insulators). Inspection data is recorded manually in log

books and files.

Since the condition indicators recorded during inspection are simplistic, it has not

been possible for TNB to make a good analysis of the data in order to find a trend

for deterioration patterns and to monitor the condition of the insulators. As a

result, TNB has not been able to determine the expected serviceable life of its

insulators in service. In effect, this situation renders its maintenance planning

process ineffective. TNB has been looking for a method or tool to improve its

insulator inspection and monitoring including its maintenance planning processes.

Powerlink Practice:

Powerlink uses a 4 category visual classification for insulator pin corrosion. These

categories are described in Table 5-2 [29]:

104

Category Rust Type Description

1 No Rust Minimal loss of galvanising, only minor

flaking and surface discoloration

2 Light Rust Loss of galvanising, surface rust evident at

cement/pin interface, no change in pin

diameter

3 Medium Rust Rust and pitting apparent, expansion of

top section of pin, rust bloom increases

the pin diameter by 10-20%, cement grout

still visible

4 Heavy Rust Rust bloom increases pin diameter by up

to 70%, cement grout no longer visible

Table 5-2: Pin corrosion description used by Powerlink

during visual inspection [29]

During visual inspection, which is done live either by climbing the tower or from

the helicopter, the inspector compares the perceived condition of the insulator

with any of the 4 categories and records it in a report form. Preventive action is

taken on insulators that have reached categories 3-Medium Rust and 4-Heavy

Rust. Powerlink stores its insulator condition based on the categories of defects in

a central computer database. Annually, its engineers assess the collected data and

make plans for insulator replacements.

As highlighted previously, the major problem with visual inspection is the high level of

variance in inspection results. This is because the nature of visual inspection heavily relies

on the personal judgement and intuition of the inspector. The inspection process as

practiced in TNB produces the highest level of variance as the judgment whether an

insulator is good or bad is solely based on the knowledge and experience of the inspector.

A fairly new staff member would interpret the level of a defect different from one who

has several years of experience. Nevertheless, a good insulator which is physically intact

and has no defect would always be reported as ‘good’ and a very bad insulator with the

entire disc broken would be interpreted as ‘bad’.

105

Powerlink’s practice of using an inspection guide reasonably narrows down the level of

subjectivity as the inspector now can compare the actual insulator as he sees it with the

defect description from the guide. However, the category numbers used are still fuzzy as

during inspection, the condition of the insulator would not exactly be the same as

described in the inspection guide. In other words, an insulator which is perceived as

category 2-Light Rust could actually be interpreted as ‘about 2’ or ‘some light rust’.

To deal with this uncertainty, the principles of fuzzy logic can be used since, as discussed

in Chapter 4 of this thesis, it was specifically developed to deal with the fuzziness of

human perception and decision making process. It also provides an organized framework

for dealing with linguistic quantifiers such as “good”, “bad”, “fair” etc. which comes up

during insulator visual defect assessment. The author of this thesis has developed a fuzzy

inference system using Matlab’s proprietary Fuzzy Logic Toolbox that can be applied to

the insulator visual inspection process.

5.2.4 The Fuzzy Inference System for Visual Inspection of Insulators

5.2.4.1 Assessment of Single Insulators

The fuzzy inference system (FIS) which the author developed here models the insulator

visual inspection process to assess rusty pins and broken porcelain discs. The respective

inspection guides for pin corrosion and broken discs as discussed in the previous section

are used to determine the linguistic variables and membership functions. Fuzzy IF-THEN

rules are used in the Inference Engine to relate the input and the output membership

functions. The Inference Engine is developed using the Mamdani inference technique and

using the steps that were discussed in Section 4.3.2. The structure of the system, which

follows the generic FIS structure shown in Figure 4-4 earlier, is shown in Figure 5-15:

106

Figure 5-15: Structure of insulator inspection FIS

The model above adopts the following steps that are taken during insulator inspection:

1. The inspector makes a visual assessment of each insulator unit in the string.

2. To check for rusty pins, the inspector compares the actual condition of the pin

and compares it with any of the four categories in Table 5-2. The actual condition

of the pin that depicts the closest to any of the four condition ratings is used as

one of the inputs to the Inference Engine.

3. To check for the condition of porcelain disc, the inspector compares the actual

porcelain condition of the insulator with the description provided in Table 5-1.

The actual condition of the porcelain disc that portrays the closest to any of the

five categories in the table is used as the other input to the Inference Engine.

4. The output of the Inference Engine indicates the condition of the insulator with

respect to the pin corrosion and porcelain insulator defect conditions. It will be

shown later that this indicator can be utilised as a measure to plan for insulator

maintenance.

The essential components of the FIS in Figure 5-15 are as follows:

Linguistic variables:

The input variables used are as follows:

o Porcelain disc condition (from Table 5-1):

No breakage, Chipped, Small Breakage, Large Breakage, Total Breakage

o Rusty pin condition (from Table 5-2):

No Rust, Light Rust, Medium Rust, Heavy Rust

Inputs

Input 1: Pin Corrosion Category

Input 2: Porcelain Disc Condition Category

Input and Output Membership Functions and Fuzzy Rules

Output: Insulator Condition

Output Inference Engine

107

The output variables used are:

o Insulator condition:

Very Poor, Poor, Fair, Good, Very Good

Membership functions:

Each of the fuzzy rules in the FIS is represented by membership functions and

IF-THEN rules, which form one half of the knowledge-base used in the FIS.

Figures 5-16, 5-17 and 5-18 below show the membership functions for the two

inputs and one output FIS used in Fuzzy Rules.

1 1.5 2 2.5 3 3.5 4

0

0.2

0.4

0.6

0.8

1

pin-corrosion

Deg

ree

of m

embe

rshi

p

no-rust lt-rust med-rust hvy-rust

Membership functions for pin rust conditions

Figure 5-16: Input membership functions for pin rust conditions

108

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Shell

Deg

ree

of m

embe

rshi

p

good chipped small-break large-break total-break

Membership functions for porcelain shell conditions

Figure 5-17: Input membership functions for porcelain shell conditions

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Condition

Deg

ree

of m

embe

rshi

p

v.poor poor fair good v.good

Membership functions for insulator condition

Figure 5-18: Output membership functions for insulator condition

The design of these membership functions follows the condition rating used

during the inspection process. For example, it has been mentioned earlier that

there are four condition ratings for pin corrosion used during inspection: ‘1-no

rust’, ‘2-light rust’, ‘3-medium rust’ and ‘4-heavy rust’. In Figure 5-16, the same

linguistic variables are used to describe the different grades of corrosion. The

condition that exactly corresponds to each of the four prescribed condition ratings

is assigned the maximum degree of membership (μ = 1.0). It can be then logically

109

inferred that those conditions that do not portray the prescribed condition in its

entirety should have lower degrees of membership (μ < 1.0). For instance, if the

condition of the pin insulator is between ‘2-light rust’ and ‘3-medium rust’, then it

should have partial memberships of either condition ratings.

As discussed in Chapter 4, membership functions can take various shapes. In this

case, it is assumed that the degree of membership decreases linearly so that the

membership functions used are triangular with base spreads not exceeding the

immediate adjacent condition rating. The reason for this is that rust on the pin

deteriorates progressively from one condition to another with time. For example,

there is no possibility that a partial rating of ‘1-no rust’ pin condition will also have

a partial rating of ‘4-heavy rust’ condition but there is a possibility that the

condition falls between 2 adjacent rating conditions, such as ‘3-medium rust’ and

‘4-heavy rust’ conditions. This makes the crossing point of each membership

function at 50% (or μ = 0.5), which has been suggested in Section 4.3.2.

The same design rationale is used for creating the other membership functions

used in Fuzzy Rules. The input membership functions for porcelain shell

conditions are given in Figure 5-17 and the output membership functions for the

resultant insulator conditions are shown in Figure 5-18.

IF-THEN Rules

The other half of the knowledge base of the insulator inspection FIS is

represented by Fuzzy Rules. Fuzzy Rules specify the relationship between the

premises and consequences used in the FIS and is expressed in terms of IF-

THEN rules; the IF-part of the rule is the premise and the THEN-part of the rule

is the consequence.

In Figure 5-15, Fuzzy Rules in the Inference Engine describes the relationship

between the different levels of rusty pins (input 1) and porcelain insulator

conditions (input 2) with the condition of the insulator unit (output). In this case,

inputs 1 and 2 are the antecedents and the output is the consequent.

110

There are four condition ratings (input 1 membership functions) that are used to

describe rusty pins and five condition ratings (input 2 membership functions) to

describe porcelain insulator conditions which can be used to infer one of the five

conditions of the insulator unit. The total number of rules needed in a FIS is

determined by the product of the number of membership functions used in the

two antecedents. In this case, a total of twenty rules are required to infer the

condition of the insulator pin. The rules can be derived using expert knowledge

and experience, survey or books/reference manuals [83]. For the FIS used here,

the author’s experience and knowledge (based on prior industry experience and

research discussed in Chapters 2 and 3 of this thesis) are used as a basis to

construct these rules. The rules are shown in Table 5-3. Note that the premises

and consequence are all indicated by linguistic variables.

For instance:

Rule 2 indicates that: IF pin corrosion is NO RUST AND porcelain shell is

CHIPPED, THEN insulator condition is GOOD. (‘NO RUST’, ‘CHIPPED’ and

‘GOOD’ are linguistic terms).

Rule 18 indicates that: IF pin corrosion is HEAVY RUST AND porcelain shell is

SMALL BREAKAGE, THEN insulator condition is VERY POOR. (‘HEAVY

RUST’, ‘SMALL BREAKAGE’ and ‘VERY POOR’ are linguistic terms).

111

Rule

IF

Pin corrosion is

AND

Porcelain shell condition is

THEN

Insulator condition is

1 NO RUST GOOD VERY GOOD

2 NO RUST CHIPPED GOOD 3 NO RUST SMALL

BREAKAGE FAIR

4 NO RUST LARGE BREAKAGE

POOR

5 NO RUST TOTAL BREAKAGE

VERY POOR

6 LIGHT RUST GOOD GOOD 7 LIGHT RUST CHIPPED FAIR 8 LIGHT RUST SMALL

BREAKAGE FAIR

9 LIGHT RUST LARGE BREAKAGE

POOR

10 LIGHT RUST TOTAL BREAKAGE

VERY POOR

11 MEDIUM RUST

GOOD POOR

12 MEDIUM RUST

CHIPPED POOR

13 MEDIUM RUST

SMALL BREAKAGE

POOR

14 MEDIUM RUST

LARGE BREAKAGE

VERY POOR

15 MEDIUM RUST

TOTAL BREAKAGE

VERY POOR

16 HEAVY RUST GOOD VERY POOR17 HEAVY RUST CHIPPED VERY POOR18 HEAVY RUST SMALL

BREAKAGE VERY POOR

19 HEAVY RUST LARGE BREAKAGE

VERY POOR

20 HEAVY RUST TOTAL BREAKAGE

VERY POOR

Table 5-3: IF-THEN rules used in the insulator inspection FIS

Combining both the input membership functions and the output membership

function with the rules above, a 3-dimensional plot can be obtained to give a

snapshot relationship between the inputs and output of the FIS as shown in

Figure 5-19 below:

112

11.5

22.5

33.5

4

1

2

3

4

5

2

3

4

pin-corrosionshell

cond

ition

Figure 5-19: A 3-dimensional plot of insulator inspection FIS showing the relationship between the two inputs and output

Defuzzification

As mentioned in Section 4.3.1, there are four common defuzzification methods

that are used in the Mamdani inference technique. The most commonly used

defuzzification method is the centroid or Centre-of-Area (COA) and is also the

method chosen for the insulator inspection FIS. Recall that the centroid can be

calculated using Eq. 4-6.

In this insulator inspection FIS, the defuzzified value is used to suggest the

appropriate maintenance action to be done on the insulator. Table 5-4 shows a list

of appropriate maintenance actions that can be taken on the insulator based on

the centroid value of the output.

Centroid value Suggested maintenance decision

1-2 Replace insulator immediately

2-3 Increase monitoring frequency

3-4 Flag for next maintenance cycle

4-5 Do nothing

Table 5-4: Suggested insulator maintenance decision

Application of the Insulator Inspection FIS

113

To test the FIS, three sample insulator conditions, which were taken from the

field, are used as inputs to the FIS.

Sample 1

Figure 5-20 shows insulator Sample 1 which was taken from the Prai-Bedong 132

kV line in the Northern part of Malaysia. From physical inspection, it was found

that there is no sign of defects on the porcelain shell but the pin seems to suffer

some corrosion. Despite a change of colour and rusty surface, the pin still retains

its original diameter.

Figure 5-20: Insulator Sample 1

Comparing the pin rust condition in Figure 5-20 above with the four pin rust

categories in Table 5-2, it is decided that the pin rust condition above falls within

pin rust categories 2-light rust and 3-medium rust; the membership value of the 3-

medium rust condition (μmedium rust(pin corrosion) = 0.8) is higher than the 2-light

rust condition (μlight rust(pin corrosion) = 0.2). This is shown in Figure 5-21:

114

Figure 5-21: Pin rust condition for insulator Sample 1

It can also be seen from Figure 6-20 that the porcelain shell condition of the

sample insulator has no defects. This means that it has a high membership of

category 1-very good porcelain shell condition (μvery good(shell) = 1.0). This is

shown in Figure 5-22:

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Shell

Deg

ree

of m

embe

rshi

p

good chipped small-break large-break total-break

Membership functions for porcelain shell conditions

Figure 5-22: Porcelain shell condition for insulator Sample 1

Referring to the rules in Table 5-3, the conditions shown in Figures 5-21 and 5-22

invoke Rule 6 and Rule 11 of the FIS.

1 1.5 2 2.5 3 3.5 4

0

0.2

0.4

0.6

0.8

1

pin-corrosion

Deg

ree

of m

embe

rshi

p

no-rust lt-rust med-rust hvy-rust

Membership functions for pin rust conditions

115

Rule 6 indicates that: IF pin corrosion is LIGHT RUST AND porcelain shell is

GOOD, THEN insulator condition is GOOD.

Rule 11 indicates that: IF pin corrosion is MEDIUM RUST AND porcelain shell

is GOOD, THEN insulator condition is POOR.

The AND operator used in the rules infers that the minimum criterion is used in

the resultant. The results of invoking rule Rules 6 and 11 are shown in Figures 6-

23 and 6-24 respectively:

Figure 5-23: Resultant of invoking Rule 6

1 1.5 2 2.5 3 3.5 4

0

0.2

0.4

0.6

0.8

1

pin-corrosion

lt-rust

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Shell

good 1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Condition

good

μlt-rust (pin corrosion) =0.2

μgood (shell)=1.0

min

μgood(condition)=0.2

116

Figure 5-24: Resultant of invoking Rule 11

The resultants of Rules 6 and 11 are then aggregated to indicate the overall

condition of the insulator. This is shown in Figure 5-25 below:

Figure 5-25: Aggregation of Rules 6 and 11 resultants

The above aggregation shown in Figure 5-25 is still represented by fuzzy

membership functions. In order to determine the overall condition of the

insulator, it is necessary to resolve the above aggregation into a crisp value. This

step is called defuzzification.

1 1.5 2 2.5 3 3.5 4 4.5 5 0

0.2 0.4 0.6 0.8

1

Condition

poor good

1 1.5 2 2.5 3 3.5 4

0

0.2

0.4

0.6

0.8

1

pin-corrosion

med-rust

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Shell

good

μmed-rust(pin corrosion)=0.8

μgood(shell)=1.0

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Condition

poor

μpoor(condition)=0.8

min

117

Applying Eq. 4-6 to the shaded areas in Figure 5-25, the centroid zCOA is found to

be 2.55 (i.e. zCOA = 2.55). Cross-referencing this value to Table 5-4, it is suggested

that the monitoring frequency of this sample insulator be increased.

Sample 2

Figure 5-26 below shows insulator Sample 2. The insulator is taken from a 132kV

line in the Southern part of Malaysia.

Figure 5-26: Insulator Sample 2

It can be seen that the insulator above is chipped-off at the outer edge of the

porcelain shell. The chip is considered to be small as the insulator is still generally

round in shape. Comparing with the category descriptions in Table 5-1, it is

decided that the shell condition corresponds to both categories 2-chipped and 3-

small breakage, with a higher membership of category 2-chipped than category 3-

small breakage. This is represented in Figure 5-27.

118

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Shell

Deg

ree

of m

embe

rshi

p

good chipped small-break large-break total-break

Membership functions for porcelain shell conditions

Figure 5-27: Porcelain shell condition for insulator Sample 2

There seems to be no signs of rust at the insulator pin which, from Table 5-2,

corresponds to category 1-no rust. This is represented in Figure 5-28.

1 1.5 2 2.5 3 3.5 4

0

0.2

0.4

0.6

0.8

1

pin-corrosion

Deg

ree

of m

embe

rshi

p

no-rust lt-rust med-rust hvy-rust

Membership functions for pin rust conditions

Figure 5-28: Pin rust condition for insulator Sample 2

Referring the above conditions as shown in Figure 5-27 and 5-28 to Table 5-3,

Rule 2 and Rule 3 are invoked.

Rule 2 indicates that: IF pin corrosion is NO RUST AND porcelain shell is

CHIPPED, THEN insulator condition is GOOD.

119

Rule 3 indicates that: IF pin corrosion is NO RUST AND porcelain shell is

SMALL BREAK, THEN insulator condition is FAIR.

Again, the AND operator in the rules implies that the minimum criterion is used in

the inference. The results of firing rules 2 and 3 are as shown in Figures 5-29 and

5-30 respectively.

Figure 5-29: Resultant of invoking Rule 2

1 1.5 2 2.5 3 3.5 4

0

0.2 0.4

0.6 0.8

1

pin-corrosion

no-rust

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2 0.4 0.6 0.8

1

Shell

chipped 1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Condition

good

μno rust(pin corrosion)=1.0

μchipped(shell)=0.7

min

120

Figure 5-30: Resultant of invoking Rule 3

The results of Rules 2 and 3 are then aggregated to indicate the overall condition

of insulator Sample 2. This is shown in Figure 5-31.

Figure 5-31: Aggregation of Rules 2 and 3 resultants

Applying Eq. 4-6 to the shaded area in Figure 5-31, the centroid is found to be

3.71 (zCOA = 3.71). Cross-referencing this value to Table 5-4, it is suggested that

insulator Sample 2 be thoroughly inspected in the next maintenance cycle.

1 1.5 2 2.5 3 3.5 4

0

0.2 0.4 0.6 0.8

1

pin-corrosion

no-rust

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2 0.4 0.6 0.8

1

Shell

small-break

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0 4

0.6

0.8

1

Condition

fair

min

μno rust(pin corrosion)=1.0

μsmall break(shell)=0.3

1 1.5 2 2.5 3 3.5 4 4.5 5 0

0.2

0.4

0.6

0.8

1

Condition

fair good

121

Sample 3

Figure 5-32 shows insulator sample 3 which had been taken down from a

Powerlink 132 kV transmission line. The insulator was removed from the line

because it appeared to have suffered severe corrosion at the pin and most of the

edge of the porcelain shell has been chipped off, but it still retains its round shape.

Figure 5-32: Insulator Sample 3

Comparing the physical condition of the porcelain shell with the category

descriptions in Table 5-1, it is suggested that the shell condition is corresponding

to full membership of category 2-chipped. This is shown in Figure 5-33.

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Shell

Deg

ree

of m

embe

rshi

p

good chipped small-break large-break total-break

Membership functions for porcelain shell conditions

Figure 5-33: Porcelain shell condition for insulator sample 3

122

As for the pin rust condition, it decided that the pin condition is corresponding to

both category 3-medium rust and category 4-heavy rust from Table 5-2, with a

higher membership of category 3-medium rust. This is shown in Figure 5-34.

1 1.5 2 2.5 3 3.5 4

0

0.2

0.4

0.6

0.8

1

pin-corrosion

Deg

ree

of m

embe

rshi

p

no-rust lt-rust med-rust hvy-rust

Membership functions for pin rust conditions

Figure 5-34: Pin rust condition for insulator sample 3

Referring to Table 5-3, the above conditions shown in Figures 5-34 and 5-35 will

invoke Rules 12 and 17.

Rule 12 indicates that: IF pin corrosion is MEDIUM RUST AND porcelain shell

is CHIPPED, THEN insulator condition is POOR.

Rule 17 indicates that: IF pin corrosion is HEAVY RUST AND porcelain shell is

CHIPPED, THEN insulator condition is VERY POOR.

The results of firing these 2 rules are shown in Figures 5-35 and 5-36 respectively.

Again, the AND operator in the rules implies that the minimum criterion is used in

the inference. The results of firing rules 12 and 17 are as shown in Figures 5-35

and 5-36 respectively.

123

Figure 5-35: Resultant of invoking Rule 12

Figure 5-36: Resultant of invoking Rule 17

The results of Rules 12 and 17 are then aggregated to indicate the overall

condition of insulator Sample 2. This is shown in Figure 5-37.

1 1.5 2 2.5 3 3.5 4

0 0.2 0.4 0.6 0.8

1

pin-corrosion

med-rust

1 1.5 2 2.5 3 3.5 4 4.5 5

0 0.2 0.4 0.6 0.8

1

Shell

chipped

min

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Condition

poor

μmedium rust(pin corrosion)=0.9

μchipped(shell)=1.0

1 1.5 2 2.5 3 3.5 4

0

0.2

0.4

0.6

0.8

1

pin-corrosion

hvy-rust

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Shell

chipped

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Condition

v.poor

μheavy rust(pin corrosion)=0.1

μchipped(shell)=1.0

min

124

Figure 5-37: Aggregation of Rule 12 and Rule 17 resultants

Applying Eq. 4-6 to the shaded area in Figure 5-37, the centroid is found to be

1.96 (zCOA = 1.96). Cross-referencing this value to Table 5-4, it implies that

insulator Sample 3 needs to be replaced immediately.

Discussion on the results of insulator inspection FIS

From the sample applications above, it has been shown that, together with an

insulator inspection guide for rusty pins and broken porcelain discs, the insulator

inspection FIS can assist visual evaluation of defective insulator units on the string

by inferring the defect level of each insulator with respect to its physical

appearance and condition. With the insulator inspection FIS, the inspector is only

required to indicate the appearance of rust at the insulator pin and

chipping/breakage of the porcelain shell by comparing these conditions with the

condition ratings used in Tables 5-1 and 5-2 respectively. With the insulator

inspection FIS, inference is undertaken by the Inference Engine of the FIS and

not by the inspector thereby relieving him/her from making any judgement with

regards to the overall condition of the insulator. Because the same knowledge-

base is used in the Inference Engine, consistency in the output is achieved.

The heart of the insulator inspection FIS is the knowledge-base that forms the

Inference Engine. In this case, the knowledge of the human expert is translated to

IF-THEN rules used in the Inference Engine. It can be argued that the level of

expertise from one human expert may differ from another such that one expert’s

design of the rules may not be the same with another but as long as they are fixed

in the Inference Engine, the outputs of the FIS will always be consistent. In the

1 1.5 2 2.5 3 3.5 4 4.5 5

0

0.2

0.4

0.6

0.8

1

Condition

v.poor poor

125

insulator inspection FIS model shown in this thesis, the author’s knowledge and

experience was directly incorporated in the Inference Engine. In the absence of an

expert, the knowledge-base can be created indirectly by knowledge engineers

using such knowledge elicitation techniques as knowledge representation and

knowledge modelling [84].

It has been shown that the insulator inspection FIS works by converting

qualitative indicators of the insulator conditions to numerical values that make it

possible to quantify the insulators’ level of defects. Apart from making it possible

to store these numerical values in a computer database for future trending and

deterioration monitoring, it has been shown that the numerical values can also be

used to suggest the maintenance crew taking appropriate maintenance actions.

Because the maintenance actions are also standardized as shown in Table 5-4,

consistency in decision-making for maintenance actions is therefore possible.

There are, however, some noticeable limitations of the insulator inspection FIS.

These include:

o Despite the use of the inspection guide, subjectivity in determining the

insulator defect level still exists as the results of the inspection now

depends on how well the inspector compares the perceived actual

insulator condition with those conditions indicated in the inspection

guide. However, in the case of TNB inspection practice, the level of

subjectivity can be significantly smaller than the level before since the

inspectors now use an inspection guide that provides a basis for them to

determine the defect level.

o The use of descriptive defect conditions in the inspection guide may not

be as effective as a pictorial representation of defect levels.

o The output of the FIS is restricted by the design and number of

membership functions and rules of the knowledge–base in the Inference

Engine. In this particular design, triangular membership functions were

used by the author to represent the linguistic terms to indicate the

different defect levels. Different membership functions such as

trapezoidal, Gaussian or sigmoid may yield different outputs. Defect

126

determination could also be improved by adding more defect levels in the

inspection guide. For example, in Table 5-2, four categories of rust levels

are used in the FIS, namely “No Rust”, “Light Rust”, “Medium Rust” and

“Heavy Rust”. By adding intermediate categories such as “Very Light

Rust”, “Light-Medium Rust” and “Medium-Heavy Rust”, the variability

between one defect level to another will become smaller. However, more

rules will need to be defined in the knowledge-base.

Recommendations

In order to improve insulator defect assessment, it is suggested that:

o TNB to initiate use of insulator inspection guide and defect assessment

criteria together with the insulator inspection FIS. TNB should also

consider doing away with manual recording of inspection data in files and

establishing a computerised database system that can be used to store and

analyse the insulator condition data. It is foreseeable that this method will

assist TNB to achieve more effective maintenance planning of its insulator

assets especially after several inspection cycles when trends in insulator

data will become apparent.

o Powerlink to continue using existing insulator inspection guide together

with the insulator inspection FIS. The FIS should be able to assist

Powerlink inspectors in improving the insulator evaluation process

because insulator conditions that fall between two defect categories can

now be quantified.

5.2.4.2 Assessment of Multiple Insulators in a String

As shown in the example assessments of insulator units in Section 5.6.1, with the insulator

inspection FIS, each insulator in a string is given a numerical rating that indicates its pin

rust and porcelain shell conditions. Depending on the defuzzified output of the FIS, the

unit is recommended to be replaced immediately, flagged for next inspection cycle or put

into closer monitoring frequency.

127

For insulator strings with rusty pins, it is very likely that the same rust conditions would

appear on each of the insulator units in the string. This is because each insulator unit in

the string is exposed to the same environmental conditions. The overall condition rating

of the string is then simply the arithmetic mean of all the individual condition ratings of

insulator units in the string. By doing this, each string is given a numerical rating that

indicates its overall pin rust conditions. This numerical rating of each string can then be

cross-referenced back to Table 5-4 for suggested maintenance action. The given numerical

rating makes it possible to store the condition indicator of defective porcelains in a

database which would be difficult to do if qualitative indicators were used. With each

inspection cycle, the database can be updated to indicate the latest condition rating of the

insulator string and this makes it possible to track the deterioration rate of the insulator

string due to pin rust.

Broken porcelain shells are easily detectible on a string as their defective appearance

would stand out in a string of perfect insulator units. Broken porcelain shells generally

occur in areas that are accessible to the public but secluded from populated places such as

residential or urban developments. This is because vandals, who cause many of the

broken porcelain shells by making them targets for rifle or slingshot practice, normally

operate in such isolated but accessible areas. Broken porcelain shells are therefore random

in nature and, unlike pin corrosion, they do not deteriorate with time and are not affected

by environmental conditions.

Chipped porcelain shells do not affect the electrical and mechanical properties of the

insulators but, as explained in Section 5.4, totally broken porcelain shells result in

reduction of the insulator’s insulating property and reduction of the insulator’s residual

mechanical strength by up to 35%. Therefore it is imperative that insulators with broken

porcelain shells be replaced immediately. The numerical rating obtained from the FIS

which infers the condition of the insulator based on the condition of the porcelain shells

can be used as a guide to replace the insulator.

For insulators that have broken porcelain shells, TNB practice is to only replace those that

are broken in the string and leave those units that are still intact on the string. On the

other hand, Powerlink practice is to replace the whole string with new insulators. Gillespie

[85] explains that the reason for this is that Powerlink’s operational policy is not to mix

128

different batches of insulator units in a string. In the author’s opinion, Powerlink’s

practice is better than that of TNB as, from the asset management perspective, having

insulator units of the same batch in the string makes it easier to track its field

performance.

5.2.4.3 Assessment of Multiple Insulator Strings in a Transmission Line

The serviceable life of a transmission line is predominantly determined by the

environment that it passes through. For the line which is installed with porcelain cap and

pin insulators, corrosion of the steel pin largely determines the remaining mechanical

strength, thus the serviceable life, of the insulators.

Steel corrosion deteriorates with time and the rate of corrosion deterioration is ascertained

by how aggressive the environment is. The aggressiveness of the site can be obtained

from a variety of sources such as:

Back tracking from actual service experience

From national or state corrosion surveys by various agencies

From national or state weather data

From actual on-site corrosion monitoring programme using tokens

From pollution severity classification recommended by the International

Electrotechnical Commission (IEC)

From best guess approximations of the relative severity of the site compared to

others (for example, a line section close to the sea would experience a higher

corrosion rate compared to one which is located in a sheltered valley).

From the above sources, it is possible to differentiate the aggressiveness of one site from

another. It is therefore possible to assign specific codes to quantify the aggressiveness of

the environment. For example, from classification of pollution severity recommended by

the IEC [86], a coding scale to identify the aggressiveness of each environment can be

introduced. This is shown in Table 5-5:

129

Environ-mental Code

Environmental Condition

General Environment

Topographical Environment

Industrial Environment

Water Environment

10 Clean All mountain areas and most agricultural areas with good rainfall

Remote from industrial and housing areas

Areas located more than 10 km from sea coast

9 8

7 Mild Agricultural areas subject to occasional periods of below average rainfall

Industrial areas with low gaseous discharge or within 2 km of sources of conductive dust

Areas located 3 to 10 km from ocean water

6 5

4 Corrosive Areas with moderate dust deposits and low rainfall but high relative humidity

High density industrial areas or within 2 km of chemical plants, fertiliser factories etc.

Areas between 1 and 3 km from ocean water or sources of soluble salts

3

2 Highly Corrosive

Arid areas with low rainfall patterns for long periods

Industrial areas which produce thick conducive deposits

Areas within 1 km of the sea coast

1

Table 5-5: Introduction of environmental coding for areas based on IEC’s pollution severity classification [86]

A more accurate representation of how severe or corrosive the environment is in a

specific location is by monitoring the rate of corrosion using sample tokens. This is done

by measuring the time it takes for the sample tokens, which are put within the area where

the tower is located, to change its state due to corrosion. This method, however, takes

time. Marshall [87] made the measurements and suggested the following codes as shown

in Table 5-6 for the rate of corrosion deterioration of zinc tokens, which corresponds to

the severity of the environment.

130

Zinc Loss Rates per Year

(G/M2/YR)

Environmental Code Environmental

Condition

6 10 Clean

7.5 8 Mild

10 6

15 4 Corrosive

30 2

60 1 Highly Corrosive

Table 5-6: Environmental coding for zinc loss [87]

The purpose of quantifying the different types of environment is so that it can be used in

a predictive model which provides an estimate of the year recommended to replace the

insulators. The model proposed is based on the assumption that the rate of corrosion

deterioration increases proportionately with the severity of the environment. With the

model, it is not necessary to establish the age of the insulators to estimate the

recommended replacement year; it only requires the condition of the insulator strings on

the line at the time of inspection and the environment in which the line section is located.

The model proposed is given in Eq. 5-1:

100

ECRCSRMLIYRY

where RY = Recommended Replacement Year

IY = Year of inspection

ML = Maximum life of insulators in a clean environment

= 50 years (refer Section 5.3)

SR = Insulator string condition rating at the time of inspection

= Arithmetic mean of the rating conditions of all insulators in the string

RC = Minimum insulator string condition rating that requires replacement

= 2 (refer Table 5-5)

EC = Environmental code

131

Note that in the insulator replacement model, the replacement criteria for the insulator

string is when its rating condition is poor (SR = 2). This is the same rating condition used

in the insulator inspection FIS that suggests immediate insulator replacement. Such a

condition warrants that the insulator be replaced immediately. For insulator string

condition rating between 2 and 5, the insulator replacement model can be used to estimate

the year it is recommended to be replaced. With regular monitoring, the accumulated

numerical data from the proposed inspection method will make it possible to improve the

estimate.

Some numerical examples of the insulator replacement model, assuming last insulator

inspection year is in 2003, are shown below to illustrate the application of the insulator

replacement model:

1. Very good condition insulators (SR = 5) in a clean environment (EC = 10)

2018

100

1025502003

RY

From the above calculation, it is recommended that the insulators be replaced in

year 2018.

2. Fair condition insulators (SR = 3) in a mild environment (EC = 7)

2007

100

723502003

RY

It is recommended that the insulators be replaced in year 2007.

3. Good condition insulators (SR = 4) in an aggressive environment (EC = 3)

2006

100

324502003

RY

It is recommended that the insulators be replaced in year 2006

For long transmission lines that traverse different types of environment, it is possible to

assign an environmental code to different sections of the line that are exposed to similar

environments. For example, a transmission line may have a total of 250 towers from end

to end. The first section of 50 towers may be located near the sea coast where the

environment is very corrosive (EC = 2). The next 50 towers may run through a

132

mountainous and/or forest areas where the environment is relatively cleaner (EC = 9).

The rest of the towers may be located near or within a residential area where the

environment is harsher than the previous one (EC = 4). It is then possible to use the

environmental codes and the insulator string condition ratings for the towers in the

respective environments to estimate the year required to replace the insulators in each

section.

The insulator replacement model makes it possible for utilities to make budgetary plans

for bulk replacement of insulators based on their condition ratings and the environment

they are operating in. For long lines, it is possible to use the model to plan for staggered

replacement of different sections of the line rather than replacing all the insulators of the

line at one time. In this way, utilities are able to achieve significant savings in capital

expenditure.

5.3 Application of the FIS on Other Transmission Line Components

To effectively manage maintenance costs, normal utility practice during an inspection

exercise is to check not just one component of the transmission line but all the

components. For example, during a tower top inspection in TNB, the inspector is

required to visually inspect all of the transmission line items in the inspection checklist

which includes the foundations, the tower body, the cross arms, the conductors (including

vibration dampers, spacers, and jumpers), the hardware fittings (such as clamps, yoke

plates, and extension links), the earth wire and, of course, the insulators.

Since the inspection is done visually, there exists the same kind of uncertainty that the

inspector encounters when assessing these components. From the experience gathered

from designing and applying the FIS to the visual inspection of porcelain insulators, the

same design principles can be used to develop and apply a knowledge-based FIS on the

other components of the transmission line. The system could take the structure outlined

in Figure 5-38.

133

Figure 5-38: Proposed structure of tower inspection FIS

The steps that need to be taken, which also exemplify the steps taken to develop the

insulator inspection as described in this thesis, are as follows:

1. Identify the function(s) of the components and their failure modes.

2. Identify the indicators of failure for each component. For example, failure of the

tower body can be indicated by how much deflected or bent a certain tower

bracings appear and failure of a wooden cross arm can be determined by how

severe the cracks on the wood surface appears. For failures that deteriorate with

time, the P-F curve of the component should be determined so that the critical

point P can be identified.

3. For each component condition, define numerical categories of defect based on

how the different levels of defect can be visually detected. Either pictorial samples

or linguistic descriptions of defect levels can be used. This also makes up the

inspection guide that is used by the inspectors during inspection.

4. Based on the respective defect levels, develop membership functions for the

inputs and outputs of the FIS.

Inputs

Insulators Porcelain Shell Breakage

Pin Corrosion

Knowledge Base 1

Conductors

Joint resistance

Joint temperature

KnowledgeBase 2

Structure Deformation

Corrosion

Knowledge Base 3

Foundations

Corrosion

Displacement

Knowledge Base 4

Knowledge Base 5 Tower

Condition

OutputInference EngineComponents

134

5. Develop fuzzy IF-THEN rules and choose the appropriate defuzzification

technique for the FIS.

6. Design a standard table of suggested maintenance actions.

Steps 1 and 2 may require tests and experiments done on the component in order to

determine the right deterioration mechanisms. The information gathered in these steps is

then transferred to the knowledge-base of the FIS. Together with the inspection guide,

the FIS can prove to be useful in the field during tower inspection exercise as not only it

can significantly reduce subjectivity associated with the inspection process but also can

make available expert knowledge at site. With the standard maintenance actions which are

inferred from the output of the FIS, the inspectors can readily suggest the appropriate

maintenance actions based on the defect condition of the components.

5.4 Potential Savings due to Introduction of FIS in Tenaga Nasional Berhad’s

(TNB) Transmission Line Inspection and Maintenance Practice

This section looks at the possible savings that can be achieved by Tenaga Nasional Berhad

(TNB) due to the introduction of improved inspection process. The currency used is in

Malaysian Ringgit (A$ 1 ~ RM 2.80) as data is from TNB.

TNB’s transmission system spans the whole of Peninsular Malaysia, connecting power

stations to load centers. The system operates at 132 kV, 275 kV and 500 kV voltage levels

and forms an integrated network known as the National Grid. Overhead transmission

lines currently make up 95.7% (16, 137 km) of the network [88].

TNB uses double-circuit steel lattice towers as the standard design for its transmission

line. Double circuit lines are used to ensure good supply reliability (N-1 operation

contingency) and to maximize land utilization for transmission line right-of-way. To

protect the transmission lines from lightning strikes, TNB uses two earth wires strung at

the top of the phase conductors. TNB also uses timber cross arms on a majority of its

suspension towers to increase line insulation.

To assess its transmission line operational performance, TNB uses a set of key

performance indicators. One of the indicators used by TNB is the transmission line

135

outage rate. Figure A-2 provides a snapshot of TNB’s transmission line forced outage rate

for the periods between 1998/1999 and 2002/2003 [88].

Figure 5-39: TNB transmission line forced outages 1997-2003 [88]

It can be seen from Figure A-2 that the annualized 275 kV and 132 kV transmission line

performance in 2002/2003 is 1.09 per 100 km-circuit and 1.03 per 100 km-circuit

respectively. Most forced outages experienced by TNB have been due to bad weather or

lighting strikes. This is not surprising because Malaysia is located in the tropics where the

isokeraunic level is about 180 thunder days per year [89].

Each year TNB spends between RM 32 million and RM 37 million on inspection and

maintenance of its transmission line [90]. This expenditure is due to current inspection

and maintenance activities which include:

Monthly routine ground patrol inspection for transmission lines located in

accessible regions

3-yearly routine climbing inspection for all transmission lines

Yearly routine climbing inspection for lines that cross roads, highways, railways

and rivers

Yearly helicopter inspection for transmission lines located in inaccessible regions

Bulk replacement of wooden cross arms and cap and pin insulators that have

been installed for 25 years

136

As with many utilities worldwide, TNB has been working towards reducing its

maintenance and inspection expenditure whilst maintaining or further reducing its outage

rate. One way of achieving this objective is to reduce the frequency of inspection and

maintenance based on the condition of the component. The improved inspection process

using the FIS as proposed in this thesis provides a means for TNB to use a numerical

rating scheme to quantify the condition of the component. Specific maintenance actions

can therefore be made on those components that are bad and/or achieving its end of

service life. Expenditures can therefore be channelled to components with such

conditions rather than on the whole component population. The net effect is savings in

inspection and maintenance expenditures.

At the same time, information regarding the condition of the components that are

collected using the FIS can be stored in a computer. As more data are collected, an

analysis of the component deterioration trend can be made. The trends would enable

TNB to make projections of future component replacement or repair activities thus taking

better control of its maintenance budget.

It is rather difficult to estimate the exact savings that can be achieved by introducing the

new inspection process, but based on the current annual transmission line inspection and

maintenance expenditure, a savings of 10% would translate to between RM 3 million to

RM 4 million a year. With time, the savings could potentially be more significant as more

data about component condition is available for future maintenance projections.

5.5 Chapter Summary

In this chapter, we have primarily presented the development of a novel methodology that

utilizes principles of fuzzy logic to handle uncertainties associated with visual inspection

of porcelain cap and pin insulators. Current practices of two utilities namely TNB

(Malaysia) and Powerlink Queensland (Australia) were discussed which indicated the need

for such a methodology to overcome the problem of uncertainty.

Together with an inspection guide, it has been shown that using the insulator inspection

fuzzy inference system significantly reduces the subjectivity of assessing insulator pin rust

137

and porcelain shell conditions. The development of the fuzzy inference system, whereby

the design of input/output membership functions and linguistic variables were utilized in

the rule base, has also been explained.

It has also been elucidated how the fuzzy inference system was used to determine the

condition of three sample insulators taken from the field. It has been further shown that

qualitative analysis based on linguistic indicators used during assessment of the insulators

is converted to crisp numbers by the fuzzy inference system. The results of the output of

the fuzzy inference system and how they can be used as a decision support for

maintenance have also been discussed.

The numerical rating of each insulator unit has made it possible to define the rating of an

insulator string. It has been shown that the numerical rating of multiple insulator strings in

a transmission line can be used in a mathematical model to estimate the appropriate

replacement date for bulk replacements due to pin corrosion deterioration.

Further in this chapter, we have also discussed the application of the FIS on visual

assessment exercise of other components of the transmission line.

Finally, we have discussed the possible savings that might be achievable if the new

inspection methodology is implemented in TNB.

138

CHAPTER 6: CONCLUSIONS AND FUTURE WORK

6.1 Summary of the Research

The main goal of the research presented in this thesis was to develop a novel

methodology that would improve utility practice of visual defect assessment of

transmission line components. In reaching this objective, this thesis seeks to answer the

research question, “How can we reduce or remove the uncertainty associated with

inspector visual assessment during field inspection so that the information gathered from

the field inspection can be used effectively to manage maintenance actions?” The answer

to this question is, as has been proposed in this thesis, a knowledge-based fuzzy inference

system. This chapter presents an overall conclusion of the research and reflects on the

objectives listed in Section 1.2.

The first stage of the study was aimed at identifying the functions and understanding the

failure modes of the major transmission line components. This was an essential part of the

study because the reason utilities carry out visual inspection was to identify defects that

could lead to imminent failure. In Chapter 2, it was highlighted that environmental factors

such as wind, weather and atmospheric pollution have a major influence on failures of

transmission line components. Except for very severe cases such as major storms with

high incidental winds or earthquakes which could result in immediate transmission line

failure, failures of transmission line components generally start with small defects followed

by gradual deterioration.

Failure, as defined in this thesis, is loss of function. In this regard, Chapter 2 presented the

main functions of the major components and their associated failure modes. These are

summarized in Table 6-1.

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Component MAIN FunctionS Failure Modes

Steel

Tower/Structure

Withstand static and

dynamic forces

Buckling or collapse due to structural

failure made worse by effects of

corrosion

Foundations Transfer mechanical loads

to ground for stability

Foundation failure due to corrosion

of steel reinforcement bar

Excessive soil movement

Conductors Carry rated current while

maintaining even sag

Conductor snap due to corrosion,

vibration fatigue or annealing

Insulators Provide mechanical

support

Broken pin due to corrosion or metal

fatigue

Withstand electrical

stresses

Damaged insulator shells

Table 6-1: Summary of transmission line components, their functions and failure modes

As shown in Table 6-1, a majority of the defects is due to deterioration of steel

components as a result of corrosion.

Having understood the functions of transmission line components and their failure

modes, the second phase of the study was to review the available methods for detecting

these defects from the various sources of literature and the third chapter includes

information about various types of component monitoring systems. It was found that,

depending on the component and its functions, a majority of these methods utilized

equipments that were able to detect either electrical or physical defects on transmission

lines. It was also found that, among the major components, the insulator has the greatest

number of different equipments available determination of its condition. However, these

equipments were either found to be expensive (i.e. the Daylight Corona camera), still in

the development stages (i.e. analysis of sample polymeric insulator sheds using Fourier

transform infrared spectroscope) or highly affected by the environment (i.e. the

thermovision camera or corona phone).

Chapter three also provided a review of inspection and maintenance practices of utilities

worldwide, which was the third objective of the research. For this purpose, the results of

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two industry surveys were analysed: one was conducted by CIGRE [1] on 90 utilities

worldwide and the other by McMahon [2] on utilities in Australia and New Zealand. The

CIGRE survey indicated that utilities regard the most typical form of defect is corrosion

attack on steel components. However, the significance of both the survey results to this

research is that, despite the advent of new monitoring equipments, both the survey results

indicated that the most widely used utility practice for location of defects on transmission

lines is by visual inspection. The method requires inspectors to visually inspect the

component either from the air (in helicopters), on the ground (using visual aids such as

binoculars or zoom cameras) or at the top of the tower (by climbing).

The main problem with visual inspection, as discussed towards the end of the chapter

three, is that there exists a high level of subjectivity and uncertainty when the inspectors

are making the defect evaluation. Another problem is that because the defects are

recognized by their perceived physical conditions, defect indicators are normally reported

qualitatively using vague and linguistic terms such as “medium crack”, “heavy rust”,

“small deflection” and “large breakage”. Unfortunately, inspectors are human beings and

not machines or robots that can be programmed to produce accurate results all the time.

As a consequence, there is a large variance in defect reporting (which, in time, makes it

difficult for utilities to monitor the condition of the component) that can lead to wrong or

ineffective maintenance decisions. This thesis recommends solving this problem by using

the artificial intelligence technique of fuzzy logic, a technique which was first introduced

in the 1960’s to deal with the fuzziness of human judgment.

The aim of chapter four in this thesis was to provide an understanding of the principles of

fuzzy logic for it to be applied in this research. In this chapter, the main building blocks of

fuzzy logic, namely the construction of membership functions and IF-THEN rules, were

discussed. It was shown in the chapter how the combination of membership functions

that represent linguistic variables and IF-THEN rules were implemented in a knowledge-

based Fuzzy Inference System (FIS), one of the ways by which fuzzy logic is used in real-

world applications. To assist in understanding further the subject matter, several

applications of fuzzy logic in the utility environment were also discussed in this chapter.

The fifth and main objective of this research was to design, apply and test a knowledge-

based FIS on the utility visual inspection of one of the most important components of the

141

transmission line – the porcelain cap and pin insulators. For this purpose, chapter five

examined two of the most common failure mechanisms of porcelain cap and pin

insulators: corrosion of the steel pin and breakage of the porcelain shells. Pin rust

increases gradually with time and the rate of deterioration is affected by the environment

wherein the insulator is located. Breakage of porcelain shells is mostly due to vandalism,

does not increase with time and normally occurs on lines that are located in areas

accessible to the public. Both types of defects are normally detected visually by utilities

during routine inspections. For the purpose of this research, current insulator inspection

practice of two utilities, Powerlink Queensland (Australia) and Tenaga Nasional Berhad

Malaysia (TNB), was studied. Table 6-2 provides a summary comparison of the inspection

methods of both utilities.

TNB powerlink

Visual inspection is done by

linesmen during routine line

outage for inspection

No inspection guides are used

Inspection results are recorded

in inspection form: (√) means

good – no action, (x) means bad

– replace immediately; anything

in between is recorded in

separate forms

Maintenance is done based on

the reported condition of the

insulator

Bulk replacement is sometimes

taken if the same defects are

detected on many insulators

Visual inspection is done by

linesmen during routine live line

inspection

For rust conditions, a 4-category

rust in inspection guide is used

(scale: 1-no rust to 4-heavy rust)

Maintenance decision is based

on the reported condition of the

insulator referred to one of four

defect categories

Bulk replacement is taken if

necessary

Table 6-2: Summary of TNB and Powerlink porcelain

cap and pin insulator inspection practice

It was deduced that the TNB practice results in high level of variance in defect reporting

due to the linesmen not using any inspection guides when conducting the inspection.

142

Powerlink practice narrows down the variance level but there is still uncertainty in the

defect evaluation because the actual perceived condition of the insulator may not be

exactly the same as described in the inspection guide.

Chapter five then proceeded with the design of a knowledge-based FIS for use during

insulator visual inspection. The first step shown was to develop defect rating tables based

on the linguistic description of the severity of porcelain shell breakage (see Table 5-1) and

pin rust (see Table 5-2). Using Matlab’s proprietary Fuzzy Logic Toolbox program, these

defect ratings were then converted to triangular membership functions. The overall

condition ratings of the insulator were also represented by membership functions. In the

FIS, the relationship between the inputs (pin rust condition and porcelain shell breakage

condition membership functions) and the output (insulator overall condition membership

function) was represented by IF-THEN rules. For this purpose, a table of twenty IF-

THEN rules (see Table 5-3) was then created to infer the overall condition of the

insulator based on its pin rust and porcelain shell conditions respectively. Mamdani’s

Center-of–Area (COA) defuzzification method was used in the FIS, where the overall

condition of the insulator was inferred by calculating the point which is central to the area

under the aggregated output membership function. Finally, the output of the FIS was

cross-referenced to a table of suggested maintenance action (see Table 5-4) to guide the

user to either “do nothing”, “flag for next maintenance cycle”, “increase monitoring

frequency” or “replace immediately”.

The insulator inspection FIS was then tested on three sample insulators taken from the

field. Experience from the three sample applications showed that:

together with an insulator inspection guide for rusty pins and broken porcelain

shells, the insulator inspection FIS assisted visual evaluation of defective insulator

units on the string by inferring the defect level of each insulator with respect to

its physical appearance and condition.

the inspector was only required to indicate the appearance of rust at the insulator

pin and chipping/breakage of the porcelain shell by comparing these conditions

with the condition ratings used in Tables 5-1 and 5-2 respectively.

inference was undertaken by the Inference Engine of the FIS and not by the

inspector thereby relieving the inspector from making any judgment with regards

143

to the overall condition of the insulator. This greatly reduced subjectivity and

uncertainty when evaluating insulator defects.

because the output of the FIS was referred to standardized maintenance actions

(see Table 5-4), consistency in decision-making for maintenance actions was

therefore possible.

Chapter five also showed how the output of the insulator inspection FIS was used in a

mathematical model that was developed to assist maintenance managers in making bulk

replacement of rusty porcelain cap and pin insulators. The assumptions used in the model

were that the rate of pin rust deterioration over time follows a linear relationship and that

the rate of pin rust deterioration is higher in areas of aggressive environments. For this

intention, an environmental rating condition based on the International Electrotechnical

Commission’s (IEC) definition of pollution severity was created. Application of the model

on several combinations of insulator and environmental conditions showed the model’s

usability as a tool for managing insulator assets, thus achieving the sixth and final objective

of this thesis.

As finally discussed in chapter five, the ideas used during the design of the insulator

inspection FIS can be applied towards the design and implementation of a transmission

line inspection FIS which takes into account the visual assessment of other transmission

line components namely the tower structure, the conductors and the foundations.

True to this thesis’s title and research objectives, thus it has been shown “The Application

of Knowledge-based Fuzzy Inference System on High Voltage Transmission Line

Maintenance”.

6.2 Major Research Contributions

Several benefits of the FIS include:

Together with visual inspection guide, FIS can effectively reduce the level of

uncertainty when assessing transmission line defects. This results in a more

objective and consistent evaluation of defects as well as provides support to

making maintenance decisions.

144

FIS works by transforming fuzzy qualitative indicators that are prevalent when

assessing defects visually to crisp numerical values that can be used as defect

rating.

Numerical values make it possible to represent transmission line component

defect condition in computer database for trending and future maintenance

strategies.

Numerical values can be used in a replacement model to plan for bulk

replacement of transmission line components.

Programmed into mobile computing devices, FIS makes available expert

knowledge at site

FIS can be fine-tuned by adjusting membership functions and IF-THEN rules

based on available expert knowledge.

6.3 Future Work

The following are recommendations for further work on this research topic, which in the

author’s opinion, will prove to be particularly fruitful:

Examining the effect of using different membership function shapes such as

trapezoidal, Gaussian or sigmoid on the output of the FIS.

Further reducing the level of uncertainty associated with visual inspection of

defects. The use of a pictorial representation of defect levels in the inspection

guide (rather than descriptions of defect as used in this research) may help achieve

this objective.

Examining the sensitivity of the knowledge-based FIS by using more defect levels

in the inspection guide. However, this would entail more rules being defined in

the FIS inference engine.

Incorporating the Matlab codes in mobile computing device platform for practical

implementation by field inspectors.

145

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