Christos Kaidis

8/11/2019 Christos Kaidis

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Wind Turbine Reliability Prediction A SCADA data processing & Reliability estimation tool

1

WIND

TURBINE

RELIABILITY

PREDICTION

A SCADADATAPROCESSING& RELIABILITYESTIMATIONTOOL

A Project by

CHRISTOS KAIDIS

Submitted to the Office of Graduate Studies of

Uppsala University

in partial fulfilment of the requirements for the degree of

WIND POWER PROJECT MANAGEMENT

September 2013

Major Subject: "Energy Technology"

Master of Science in Wind Power Project Management

2013, Visby, Sweden


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WINDTURBINERELIABILITYPREDICTIONA SCADADATAPROCESSING& RELIABILITY

ESTIMATIONTOOL

A Project by

CHRISTOS KAIDIS

Submitted to the Office of Graduate Studies of

Uppsala University

in partial fulfilment of the requirements for the degree of

WIND POWER PROJECT MANAGEMENT

Approved by:

Supervisors: Associate Professor Bahri Uzunoglu (Uppsala University, Campus Gotland)

Filippos Amoiralis (Engineering Consultant, MECAL Independent eXperts BV)

Examiner:Professor Jens Srensen (D.T.U)

September 2013

Major Subject: "Energy Technology"


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ABSTRACT

This research project discusses the life-cycle analysis of wind turbines through the processing

of operational data from two modern European wind farms. A methodology for SCADA data

processing has been developed combining previous research findings and in-house experience

followed by statistical analysis of the results. The analysis was performed by dividing the

wind turbine into assemblies and the failures events in severity categories. Depending on thefailure severity category a different statistical methodology was applied, examining the

reliability growth and the applicability of the bathtub curve concept for wind turbinereliability analysis. Finally, a methodology for adapting the results of the statistical analysis to

site-specific environmental conditions is proposed.


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ACKNOWLEDGEMENTS

First of all, I would like to thank my supervisors Professor Bahri Uzunoglu and Mr. Filippos

Amoiralis for their support and encouragement throughout my research.

Additionally, I would like to thank all Mr. Eric Kamphues for giving me the opportunity to

carry out my research in MECAL Independent eXperts and his assistance during my stayingin MECAL.

I would also like to thank all my colleagues in MECAL Independent eXperts and the other

departments of MECAL for always being willing to answer my questions and add to my

thesis with their knowledge and experience.

Following, I would like to express my gratefulness to my family for their continuous love and

support the last 25 years of my life.

I would also like to thank all the teachers and personnel in Uppsala University (Campus

Gotland) for contributing to my theoretical tuition and personal development during my

Masters studies.

Last but not least, I would like to thank each and every one of my classmates in the Wind

Power Project Management 2012-2013 Masters Programme for making my staying in Visby

so memorable and full of precious moments.


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NOMECLATURE

AMSAA:Army Material Systems Analysis Activity

EWEA: European Wind Energy Association

FMECA: Failure Mode Effects and Criticality Analysis

HPP:Homogenous Poisson Process

ISET: Institut fr Solare Energieversorgungstechnik

MTBF:Mean Time Between Failures

MTTF:Mean Time To Failure

MTTR:Mean Time To Repair

NASA: National Aeronautics and Space Administration

NHPP:Non Homogenous Poisson Process

O&M:Operation & Maintenance

OEM: Original Equipment Manufacturer

PLP:Power Law Process

SCADA: Supervisory Control and Data Acquisition

WMEP: Wind Measurement & Evaluation Programme

WTG: Wind Turbine Generator


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TABLE OF CONTENTS

ABSTRACT ...........................................................................................................................3

ACKNOWLEDGEMENTS ....................................................................................................4

NOMECLATURE ........................................................................................ ..........................5

LIST OF FIGURES ................................................................................................................8

LIST OF TABLES .................................................................................................................9

1 Introduction .................................................................................................................. 10

2 Basics of reliability theory ............................................................................................ 13

2.1 Definitions ............................................................................................................. 13

2.2 Statistical tools in reliability engineering ................................................................ 14

2.2.1 Probability distributions .................................................................................. 14

2.2.2 Point process ................................................................................................... 18

3 Previous research on wind turbine reliability ......................................... ........................ 20

3.1 Reliawind ............................................................................................................... 20

3.2 WMEP ................................................................................................................... 21

3.3 Swedish wind turbine statistics ............................................................................... 22

3.4 Finnish wind turbine statistics ................................................................................ 23

3.5 WindStats (Germany & Denmark) .......................................................................... 24

4 WTG Operational Data ................................................................................................. 26

4.1 General wind farm information & Operational data types .................... ................... 26

4.1.1 General information ........................................................................................ 26

4.1.2 O&M Data ...................................................................................................... 274.1.3 Additional sources of information .................... ......................................... ...... 28

4.2 Dataset for current research .................................................................................... 28

5 Data processing ............................................................................................................ 29

5.1 Taxonomy selection for failure data .............................. ......................................... . 29

5.1.1 ReliaWind taxonomy description .................................................. ................... 29

5.2 Failure definition ................................................................................................... . 30

5.3 SCADA data processing algorithm ......................................................................... 32

5.3.1 Turbine State ................................................................................................... 33

5.3.2 Short Running Periods .................... ......................................... ........................ 34

5.3.3 Event Indicators .............................................................................................. 34

5.3.4 Downtime Events ............................................................................................ 36

5.3.5 Alarm Logs ..................................................................................................... 37

5.3.6 Failure division to WTG assemblies ................................................................ 38

6 Failure statistical analysis ............................................................................................. 41


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6.1 Overall results ........................................................................................................ 41

6.2 Statistical analysis .................................................................................................. 45

6.2.1 Recent research ............................................................................................... 45

6.2.2 Methodology proposal .................... ......................................... ........................ 46

7 Prediction model ........................................................................................................... 52

7.1 Factors influencing wind turbine reliability ............................................................ 52

7.2 Influence of environmental factors ......................................................................... 53

7.3 Model development .................... ........... ......................................... ........................ 54

7.4 Application example............................................................................................... 55

8 Closure ......................................................................................................................... 57

8.1 Conclusions ............................................................................................................ 57

8.2 Limitations ............................................................................................................. 57

8.3 Future work ............................................................................................................ 58

9 References .................................................................................................................... 59APPENDIX I ........................................................................................................................ 62

APPENDIX II ...................................................................................................................... 72


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LIST OF FIGURES

Figure 1. Global annual installed wind capacity 1996-2012, (GWEC, 2013) ...................................... 10Figure 2. Global cumulative installed wind capacity 1996-2012, (GWEC, 2013) .............................. 10

Figure 3. Typical cost breakdown for an offshore wind farm in shallow water, (Zhang, et al., 2012) . 11

Figure 4. Relation between MTBF, MTTF & MTTR (Tavner, 2009) .......................................... .......... 14

Figure 5. Distribution function F(t) and probability density function f(t), (Rausand & Hyland, 2004)............................................................................................................................................................... 15Figure 6. Reliability function, (Rausand & Hyland, 2004) ............................................... .................. 15Figure 7. Repairable system failures with interarrival times t, (Reliability EDGE, 2004) ................... 17

Figure 8. Times to failure of a non-repairable system, (Reliability EDGE, 2004) ............................... 17

Figure 9. Normalized failure rate of assemblies - Reliawind, (Wilkinson & Hendriks, 2011) ............. 20Figure 10. Normalised hours lost per turbine per year for assemblies - Reliawind, (Wilkinson &

Hendriks, 2011) ................................................. .................................................. .................................. 21Figure 11. Share of main components of total number of failures - WMEP, (Hahn, et al., 2006) ........ 21

Figure 12. Failure frequency and downtimes of components - WMEP, (Hahn, et al., 2006) ............... 22

Figure 13. Distribution of number of failures for Swedish wind power plants (2000-2004), (Ribrant &

Bertling, 2007)................................................... .................................................. .................................. 22Figure 14. Percentage of downtime per component in Sweden (2000-2004), (Ribrant & Bertling,

2007) ................................................ .................................................. .................................................... 23Figure 15. Distribution of number of failures - Finland, (Stenberg & Holttinen, 2010) ...................... 23Figure 16. Distribution of downtime - Finland, (Stenberg & Holttinen, 2010) .................................... 24

Figure 17. Variation of the failure rates of WTG assemblies for the two populations, (Tavner, et al.,2007) ................................................ .................................................. .................................................... 24

Figure 18. Reliability growth curve using the PLP for the two populations, (Tavner, et al., 2007) ..... 25Figure 19. Reliability characteristics for different subassemblies in the WMEP programme dividing

faults into minor and major failures (Faulstich, et al., 2010) ............................................. .................. 31

Figure 20. Possible states of a WTG ................................................. .................................................. .. 33Figure 21. Event Indicators when the WTG state changes ................................................. .................. 35

Figure 22. Downtime event categorization ......................................... .................................................. 36

Figure 23. Overview of the process ..................................................... ................................................ .. 40Figure 24. Results - Normalized failure frequency for wind turbine assemblies (including only

identified failures) ................................................................................................................................. 41Figure 25. Normalized downtime for wind turbine assemblies (including only identified failures) ..... 43Figure 26. The bathtub curve shape of failure rate over time for a system's lifetime, (Rausand &

Hyland, 2004) .................................................. .................................................. .................................. 45Figure 27. Main shaft set failure rate plot, (Andrawus, 2008) ............................................................. 46

Figure 28. Failure occurrence for 2 operational wind farms, (Buckley, 2013) .................................... 46Figure 29. Failure rate function plot, Manual Restarts .............................................. .......................... 48

Figure 30. Failure rate function plot, Minor Repairs ......................................... .................................. 49Figure 31. Failure rate function plot, Minor Repair - Pitch system ........................................... .......... 49

Figure 32. Weibull plot for major failures of Frequency converters of wind farm A ........................... 50

Figure 33. Weibull plot for major failures of Pitch system of wind farm A .......................................... 51Figure 34. 10-minute SCADA database analysis showing failure rate and downtime as function of

Mean wind speed, (Wilkinson, et al., 2012) ................................................................ .......................... 53

Figure 35. 10-minute SCADA database analysis showing failure rate and downtime as function ofaverage Turbulence intensity, (Wilkinson, et al., 2012) .............................................. .......................... 53Figure 36. Failure rate function plot, Manual restarts - Target wind farm .......................................... 56


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LIST OF TABLES

Table 1. Relationship between F(t), R(t), f(t) and (t), (EPSMA, 2005) ................................................ 16Table 2. General wind farm information ............................................. ................................................ .. 26

Table 3. Alternative sources of information .......................................................................................... 26

Table 4. O&M Data types and info derived .......................................................................................... 27

Table 5. Additional sources of information ........................................................................................... 28Table 6. Dataset used for this project ................................................................................................... 28Table 7. Examples of the ReliaWind taxonomy ..................................................................................... 30Table 8. Failure severity categories ...................................................................................................... 32

Table 9. Example of turbine state definition according to SCADA counters ........................................ 34

Table 10. Example of ignored short running period for a detected event ............................................. 34Table 11. Example of event indicators .................................................................................................. 35

Table 12. Example of Repairing action ................................................................................................. 36Table 13. Example of failure analysis results for one wind turbine ............................................ .......... 38

Table 14. Results - Normalized failure frequency (over the total number of failure events) ................ 42

Table 15. Normalized downtime for wind turbine assemblies (over the total number of failure events)

............................................................................................................................................................... 44Table 16. PLP model parameters for Manual Restarts ............................................... .......................... 47

Table 17. PLP model parameters for Minor Repairs .................................................. .......................... 48Table 18. PLP model parameters, Minor repair - Pitch system .................................................. .......... 49Table 19. Parameters of PLP model for manual restarts & environmental conditions ........................ 55


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1 Introduction

The usage of wind to generate energy has its roots thousand years ago when it was used for

domestic purposes such as milling grain. The electricity generation from wind started in the

beginning of the previous century but it was only after the 80s that large-scale production

projects would be realized (Boyle, 2004). The following decades the wind power sector has

shown a continuous growth with the global installed capacity growing every year since 1996as shown in Figure 1 and the global installed capacity approaching 300GB (Figure 2).

Figure 1. Global annual installed wind capacity 1996-2012, (GWEC, 2013)

Figure 2. Global cumulative installed wind capacity 1996-2012, (GWEC, 2013)

With the continuous growth of wind power efforts have been made to optimize the cost of all

the aspects constituting a wind power project (Figure 3). In general, O&M costs constitute a

sizeable share of the total annual costs of a WT. For a new machine, O&M costs might easily

have an average share over the lifetime of the turbine of approximately 20%-25% of totallevelized cost per kWh produced as long the WT is fairly new, the share might constitute

10%-15% increasing to at least 20%-35% by the end of its life (EWEA, 2010).


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Figure 3. Typical cost breakdown for an offshore wind farm in shallow water, (Zhang, et al., 2012)

The estimation of the cost of some aspects of O&M is straight forward (e.g. scheduled

service) but for the unscheduled service and the spare parts replacements the prediction

becomes more complicated. This leads OEMs (Original Equipment Manufacturers), Research

institutes and wind energy consultancies to develop estimation tools for the estimation of

O&M cost. One of these tools was developed by MECAL B.V.

The MECAL O&M Cost Forecasting Model is a probabilistic model that provides insights in

the estimated future wind farm O&M Costs. The Model has been developed by combining

years of experience from operations, maintenance, performance monitoring, and wind farm

development. During the modelling a most reliably as possible scenario is analysed to

simulate the real world. The model is based on the Monte Carlo Simulation and provides

guidance during O&M strategy development. Based on a large set of inputs, the Modelperforms a multiple number of iterations which results are then combined to provide a range

of the probable outcomes.

The thesis is addressing the multi-variable modelling of MECALs O&M Cost Forecasting

Model. The focus of the project is to develop and implement a methodology performing the

following main tasks:

Process the operational data available in MECAL

Model the reliability of wind turbine assemblies so that the results will be used as

input to the O&M Cost Forecasting Model

2%

33%

3%24%

23%

15%

Cost Breakdown

Management

Turbine

Decommissioning

Support structure

O&M

Grid connection


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Thesis overview

Chapter 2

In this chapter the theoretical background of the current research is presented. Initially, the

definitions of the key terms used are provided. Following, basic elements of statistics and

their application in reliability engineering are presented.

Chapter 3

This chapter provides a brief description of previous research on wind turbine reliabilityfocusing on research topics that have used a quantitative approach, i.e. analysing operational

data from real wind farms. The main findings of each research project are presented in this

chapter some of which will be later on used for comparison with the results of the current

research.

Chapter 4

In this chapter the operational data types available for a wind farm are initially presented. A

brief explanation of the information but also the disadvantages and problems that may occur

by using each data type is derived. Following, information of the dataset used for the analysis

in this research project is provided.

Chapter 5

For the extraction of failure information from 10-minute SCADA data an algorithm was

developed in VBA (Visual Basic for Applications). In this chapter a detailed description of

the steps followed for the development of the SCADA data processing algorithm is provided.

Initially, the selection of the Wind turbine taxonomy is justified. Following, the key points of

the methodology are explained. Additionally, other sources of operational data that have been

used for verification of the algorithm are mentioned.

Chapter 6

The extraction of failure information from SCADA data, as developed in the previous chapter,

is followed by the statistical analysis of the results. Initially, overall cumulative results

concerning the failure occurrence and downtime for each turbine assembly are presented in

this chapter. Following, the different statistical methodologies used depending on the failure

type are described.

Chapter 0

Recent research has been carried out in order to define the impact of environmental factors on

WTG reliability. In this chapter an overview of the findings of this research is provided.

Additionally, an effort to adapt the results of the statistical analysis performed in the previous

chapter to case specific analysis according to the different environmental conditions is

presented.

Chapter 8

In the final chapter the findings of this research project are summarized, the main conclusions

are presented, the limitations of the scope of work are pointed out and suggestions for future

research based on the present one are made.


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2 Basics of reliability theory

Chapter summaryIn this chapter the theoretical background of the current research is presented. Initially, the

definitions of the key terms used are provided. Following, basic elements of statistics and

their application in reliability engineering are presented.

2.1 Definitions

The definitions for this section were adopted from the two following reports where the reader

can refer for a wider list of definitions. In this list only the definitions of the terms used in this

project are included.

Reliability of wind turbine subassemblies (Spinato, et al., 2009)

Common reliability analysis methods and procedures (Barbati, 2009)

Reliability: The probability that it will perform its required function under stated conditions

for a specified period of time (Spinato, et al., 2009).

Failure: The inability of a subassembly to perform its required function under defined

conditions; the item is then in a failed state, in contrast to an operational or working state

(Spinato, et al., 2009).

Failure Mode and Effects Analysis: Analysis used to determine what parts fail, why they

usually fail and what effect their failure has on the system (End Item) (Barbati, 2009).

Repair action: Can be an addition of a new part, exchange of parts, removal of a damaged

part, changes or adjustment to settings, software update, lubrication or cleaning (Spinato, et

al., 2009).

Non-repairable system: A system which is discarded after a failure. Examples of non-

repairable systems are small batteries or light bulbs (Spinato, et al., 2009).

Repairable system: A system that, when a failure occurs, can be restored into operational

condition after any action of repair, other than replacement of the entire system. Examples of

repairable systems are WTs, car engines, electrical generators and computers (Spinato, et al.,

2009).

Mean time between failures: This term defines the mean time between failures expressed in

hours of operations for a specific module population. It does NOT mean that a module will

operate for that many hours before failure (Barbati, 2009).

Mean time to failure: This value is very similar to MTBF and is used when evaluating non-

repairable systems. MTBF assumes that a device is to experience multiple failures in a

lifetime, and after each failure a repair occurs. For non-repairable systems, there is no repair.

Therefore, in the lifetime of a non-repairable device, the device fails once and MTTF

represents the average time until this failure occurs (Barbati, 2009).


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Mean time to repair: This term defines the expected mean value of an items repair time

(Barbati, 2009).

The relation between MTBF, MTTF and MTTR is illustrated in Figure 4.

Figure 4. Relation between MTBF, MTTF & MTTR (Tavner, 2009)

Manual Restart: Failure event that requires the physical presence of crew at the turbine, in

order to reset the turbine controller after it has been tripped by an alarm (Wilkinson &

Hendriks, 2011).

Minor Repair: Failures caused by minor faults, typically involving sensor or instrumentation

failure. Replacement of small parts may be necessary as may some level of trouble-shooting

in order to isolate the problem (Wilkinson & Hendriks, 2011).

Major Repair: Failure for which more extensive work is required, usually to one of the major

mechanical components of the turbine (Wilkinson & Hendriks, 2011).

2.2

Statistical tools in reliability engineering

The main references for this overview of engineering statistics where the reader can refer for

further viewing are:

System reliability theory (Rausand & Hyland, 2004)

The new Weibull handbook (Abernethy, 2001)

Offshore wind turbines. Reliability, availability and maintenance (Tavner, 2012)

2.2.1 Probability distributions

If the time to failure T is continuously distributed the cumulative distribution functionis:

() = Pr( ) = ()

> 0

Theprobability density functionf(t) is defined as:


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() = ()

A schematic description of the cumulative distribution function and the probability density

function is given in Figure 5.

Figure 5. Distribution function F(t) and probability density function f(t), (Rausand & Hyland, 2004)

The reliability functionis:

() = 1 () = Pr( > ) > 0

Figure 6. Reliability function, (Rausand & Hyland, 2004)

Thefailure rate function(t) is:

() =()()The relationship between the distribution function, the probability density function, reliability

function and failure rate function is illustrated in


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Table 1. Relationship between F(t), R(t), f(t) and (t), (EPSMA, 2005)

2.2.1.1

Weibull distribution

The Weibull distribution was invented in 1937 by the Swedish scientist Waloddi Weibull and

it has been applied to several engineering problems since then. Some of the advantages of the

Weibull distribution are (Abernethy, 2001):

The ability to provide reasonably accurate failure analysis and failure forecasts with

small data samples

It provides a simple and useful graphical plot

It can be useful even with inadequacies in the data

For the Weibull distribution the distribution function is (Rausand & Hyland, 2004):

() = Pr( ) = 1 ()The probability density function:

() = ()The reliability function:

() = () The failure rate function:

() = Where (lamda) is the scale parameter and (alpha) the shape parameter.

In reliability engineering the Weibull distribution is used to describe one lifetime of a

component and does not allow for more than one failure. Thus, it is required that no failures

have occurred before time t and after each failure the component is as good as new has


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subsequently been replaced by a new component (Crow, 2004). Given these conditions, the

Weibull distribution (or other distribution types) is suitable for reliability analysis of non-

repairable systems. One of the common mistakes is analysing inter-arrival data of failures for

repairable systems (Reliability EDGE, 2004). The difference between time to failure for non-

repairable systems and inter-arrival time of failures of repairable systems is shown in Figure 7

& Figure 8.

Figure 7. Repairable system failures with interarrival times t, (Reliability EDGE, 2004)

Figure 8. Times to failure of a non-repairable system, (Reliability EDGE, 2004)

2.2.1.2 Exponential distribution

The exponential distribution can be considered a special case of the Weibull distribution for

shape parameter =1. By replacing =1 in the equations presented above for the Weibull

distribution (Rausand & Hyland, 2004):

Probability density function:

() = Reliability function:

() = Failure rate function:

() =


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2.2.2 Point process

A point process is a stochastic model describing the occurrence of discrete events in time or

space. In reliability analysis, failures of repairable systems can be described with point

processes (Tavner, 2012).

A random variable N(t) that represents for example the number of failure events in theinterval [0,t] is called the counting random variable. Subsequently, the number of events in

the interval (a,b] will be:

(, ] = () ()The point process mean function (t) is the expected number of failures in the interval

throughout time t:

() =[()]The rate of occurrences (t) is the rate of change of expected number of failures:

() = ()

2.2.2.1

Non-homogenous Poisson process (NHPP)

A point process is a non-homogenous Poisson process with rate of occurrence (t) if

(Rausand & Hyland, 2004):

1. (0) = 0

2.

{(), 0} 3.

Pr( + ) () 2 = ()4.

(( + ) () = 1) = () + ()

If (t) is constant then the process is a homogenous Poisson process (HPP).

2.2.2.2 Power Law Process (PLP)

The Power Law Process (also called AMSAA model) is a non-homogenous Poisson processfor which the rate of occurrence is (NIST/SEMATECH, 2012):

() = This model became popular for reliability analysis for several reasons some of which are

mentioned below (Crow, 2004):


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It introduces the concept of minimal repair. In a system with several failure modes, the

repairing action for a single failure mode is considered to bring the system to the state

it was before the failure (or to an as-bad-as-old state).

If the time to the first follows the Weibull distribution (with shape parameter andscale parameter ) each succeeding failure follows the Power Law model assuming a

minimal repair. For this reason the Power Law model is also called Weibull Process,

though this name should be avoided as it can create misconceptions(NIST/SEMATECH, 2012).

It is easy to use and understand.

For the Power Law the waiting time to the next failure, given a failure at time T, has

distribution function (NIST/SEMATECH, 2012):

() = 1 [()]

Power Law model parameter estimation

A general maximum likelihood estimation for the parameters of the Power Law model is

given by Crow in the AMSAA report No. 138 (Crow, 1975). The parameters (,) are

calculated by the equations:

=

( )

=

[ ] ()

: : : : :


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3 Previous research on wind turbine reliability

This chapter provides a brief description of previous research on wind turbine reliability

focusing on research topics that have used a quantitative approach, i.e. analysing operational

data from real wind farms. The main findings of each research project are presented in this

chapter some of which will be later on used for comparison with the results of the current

research.

3.1 Reliawind

The Reliawind project is among the most recent research project on wind turbine reliability

finalised in 2011 with the participation of major wind turbine manufacturers, operators and

research institutes (Gamesa, Alstom and GL Garrad Hassan to mention some) (Reliawind,

2011). The database of Reliawind contained (in the last update released) data from around 350

operating WTGs of rated power larger than 850kW for varying periods of time;

approximately 35000 downtime events have been identified (Wilkinson, et al., 2011). In

Error! Not a valid bookmark self-reference.& Figure 10 are shown the normalised results

of Reliawind concerning the failures of wind turbine assemblies.

Figure 9. Normalized failure rate of assemblies - Reliawind, (Wilkinson & Hendriks, 2011)


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Figure 10. Normalised hours lost per turbine per year for assemblies - Reliawind, (Wilkinson & Hendriks, 2011)

3.2 WMEP

The Wind Measurement & Evaluation Programme (WMEP) was of the most direct and

sizeable efforts to monitor wind turbine reliability with the participation of 1467 WTGs in the

period from 1989 until the end of 2004 (Langniss, 2006). The rated power of the majority of

the wind turbines participating in the programme was below 1MW (Hahn, et al., 2006). A

detailed table with all the turbines participating in WMEP is given in APPENDIX II.

In Figure 11 & Figure 12 the main results of WMEP concerning the failure frequency and

downtime of components are presented.

Figure 11. Share of main components of total number of failures - WMEP, (Hahn, et al., 2006)


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Figure 12. Failure frequency and downtimes of components - WMEP, (Hahn, et al., 2006)

3.3 Swedish wind turbine statistics

A reliability study for Swedish wind power plants during the period 1997-2005 was carried

out as part of the research of the Royal Institute of Technology of Stockholm on wind turbine

O&M (Ribrant, 2006). The research contained data from 723 wind turbines from Sweden and

apart from the general reliability analysis gave a closer insight to gearbox failures which were

considered critical because the long downtime per gearbox failure found in the initial results

(Ribrant & Bertling, 2007). The main results concerning failure frequency and downtimes are

shown in Figure 13 & Figure 14.

Figure 13. Distribution of number of failures for Swedish wind power plants (2000-2004), (Ribrant & Bertling, 2007)


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Figure 14. Percentage of downtime per component in Sweden (2000-2004), (Ribrant & Bertling, 2007)

3.4 Finnish wind turbine statistics

In Finland, an effort to monitor the failures of the countrys wind turbines was made by VTT.

In this research project data from 72 operating wind turbines of Finland between the years

1996-2008 were used. The results of the project show the percentage of failures and downtime

for each wind turbine component. Additionally, an effort to distinguish the main root causes

for failures is made (Stenberg & Holttinen, 2010). The overall results are presented in

Figure 15 & Figure 16.

Figure 15. Distribution of number of failures - Finland, (Stenberg & Holttinen, 2010)


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Figure 16. Distribution of downtime - Finland, (Stenberg & Holttinen, 2010)

3.5 WindStats (Germany & Denmark)

Efforts were made to analyse failure data gathered from the Windstats newsletter. Windstats

Newsletter is a quarterly international wind energy publication with news, reviews, and WT

production and operating data from thousands of WTs published as a supplement to the

magazine Windpower Monthly (Faulstich, et al., 2009). The data contained wind turbines

from Germany and Denmark and were analysed separately for each country. The overall

results are presented in Figure 17. Apart from general results, the Windstats data were used

for statistical analysis using the Power Law process in order to track the reliability growth of

the wind turbines in Germany and Denmark as shown in Figure 18.

Figure 17. Variation of the failure rates of WTG assemblies for the two populations, (Tavner, et al., 2007)


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Figure 18. Reliability growth curve using the PLP for the two populations, (Tavner, et al., 2007)


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4 WTG Operational Data

In this chapter the operational data types available for a wind farm are initially presented. A

brief explanation of the information but also the disadvantages and problems that may occur

by using each data type is derived. Following, information of the dataset used for the analysis

in this research project is provided.

4.1 General wind farm information & Operational data types

4.1.1 General information

General information about the wind farms is also needed in order to classify failures through

operational data. The information needed and its functionality is presented in Table 2.

Information Functionality

Wind farm/Wind turbine

info

Number of WTGs in wind

farm

Failure classificationRated power

Manufacturer

Components type

Site conditions

Monthly mean wind speedConnect Failures-Environmental

ConditionsTurbulence intensity

Terrain roughness

OtherO&M provider

O&M EvaluationQuality of service

Table 2. General wind farm information

For some of the elements mentioned above, alternative methods to derive information are

proposed in Table 3.

Information Alternative Functionality

Site conditions Wind farm location Derive environmental conditions information

from meteorological databases & maps

Rated power Power groups (e.g. 1-1,5 MW) Failure classificationTable 3. Alternative sources of information


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4.1.2 O&M Data

O&M data can be acquired in the following forms:

a) Maintenance logs

b)

Operation & Alarm logs

c)

10-minute SCADA& Alarmsd)

Questionnaire

e)

Service provider bills

f)

Component purchase bills

In Table 4 the above mentioned O&M data forms are sorted in priority order (starting from

the most useful), followed by the information that can be derived and the drawbacks of each

one.

O&M Data type Information derived Disadvantages

A. Maintenance logs

Accurate failure info

Information for downtimes Cost of repair

Sometimes available only

in hardcopies Can be difficult to read or

incomplete

B.Operation & Alarm logs Failures and duration

Unknown alarm codes

No environmental

conditions info

Numerous stops for the

same failure

C.10-minutes SCADA &

Alarms

Failure data

Environmental parameters

Information for further analysis

(e.g. Root cause analysis) Comparison/verification of logs

(if both available)

Large amount of data,

require time-consuming

processing

Not all alarms indicate

failures No maintenance activity

described

D.Service provider bills

Maintenance cost

Indications for the kind of

failures

Less detailed info about

failures

E.Component purchase

bills

Information for component

replacements

No downtime information

No failure information

Table 4. O&M Data types and info derived

.


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Concerning 10-minute SCADA, in order to avoid a large amount of data that is difficult to be

transferred and processed specific columns can be requested instead of the whole data set.

More specifically, the following columns of the SCADA data contain the information initially

needed for the present research:

1.

Ambient wind speed

2.

Grid production power3.

Hour counters, Service On

4.

Hour counters, Turbine OK

5.

Hour counters, Alarm Active

6. Ambient wind speed standard deviation

4.1.3 Additional sources of informationThe O&M data types mentioned in Table 4 can provide reliability information of varying

accuracy and level of detail. In Table 5 additional information sources are presented; these

information sources cannot be used to derive failure rates but as complementary to the above

mentioned and/or for verification of the results.

Source Information derived

1.Turbine availability figures Overall availability

Rough estimation of failure rates

2.Inspection reports

Indications of future failures

Time span between failure indication and actual

failure

Table 5. Additional sources of information

4.2

Dataset for current research

For this project, SCADA data from two European wind farms were provided from MECAL

B.V. The WTG type, population and time length of data are different for the two wind farms.

Due to confidentiality reasons, details about the location, turbine manufacturer and type will

not be provided. The two wind farms will be named as A & B. It should be also mentioned

that for wind farm B the time length of the data was not the same for all the turbines.

Information concerning the dataset used is presented in Table 6.

Wind farm Number of WTGs Rated Power (kW) Days of data Turbine*days

A 23 3000 944 21712

B 36 850-1750 760 25381Table 6. Dataset used for this project


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5 Data processing

For the extraction of failure information from 10-minute SCADA data an algorithm was

developed in VBA (Visual Basic for Applications). In this chapter a detailed description of the

steps followed for the development of the SCADA data processing algorithm is provided.

Initially, the selection of the Wind turbine taxonomy is justified. Following, the key points of

the methodology are explained. Additionally, other sources of operational data that have beenused for verification of the algorithm are mentioned.

5.1 Taxonomy selection for failure data

Wind turbine taxonomy is a structure that names the main features of a WTG in a

standardised terminology (Tavner, 2011). The definition of a taxonomy before starting a

WTG reliability research project is necessary in order to de fine accurately failure locations

and also describe turbines from different WTG manufacturers in a common way (Wilkinson,

2011). There have been several efforts of developing a Wind turbine taxonomy differing on

their principal structure and level of detail. The main criteria for the development of thesetaxonomies have been the information availability (so that the level of detail of the taxonomy

will correspond to the level of detail of the information available) and the function of each

component with the components performing the same function grouped together (Ribrant,

2006).

For this research the taxonomy developed for the ReliaWind project will be used. The reasons

for this selection were the following:

The ReliaWind taxonomy was developed in order to focus on SCADA and Service

Log data (Tavner, 2011, p. 9), a fact that meets the needs of this project which will

focus on SCADA data and Alarm Logs for reliability analysis.

It divides the WTG in categories of various detail (with the most detailed level

containing 257 WTG components) offering the potential of statistical analysis in a

more detailed level when the amount and accuracy of operational data will permit.

It was developed with the assistance of two major WTG operators (GL Garrad Hassanand Eon Climate and Renewables) indicating that it will be widely used in the future.

It should be mentioned that there is also another wind turbine taxonomy with high level of

detail developed by Sandia National Laboratories (Peters, et al., 2009). The reason that the

ReliaWind taxonomy was preferred is that it was also applied in data analysis in the context

of the ReliaWind project.

5.1.1 ReliaWind taxonomy description

The ReliaWind taxonomy is structured based on the following guidelines (Tavner, 2011):

The taxonomy includes all the WTG concepts components in 5 levels, namely:

o System

o Sub-System

o Assembly

o Sub-Assembly


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o

Component

A hybrid approach is applied for the components grouping; signalling, supervisory and

control components are grouped according to their function and mechanical

components are grouped according to their position in the WTG.

The taxonomy characteristics described above are clarified in the examples shown in Table 7.The mechanical components are grouped according to their position (e.g. all the components

of the gearbox are grouped together) and the electrical components according to their function

(e.g. all the sensors placed in different locations of the turbine are grouped together in the

Control & Communication System assembly).

System Sub-System Assembly Sub-Assembly Component

WTG Drive Train Module Gearbox Bearings Planet Bearing

WTG Nacelle Mode Yaw System Yaw Brake Yaw Brake Disc

WTG Electrical Module

Control and Communication

System

Condition

Monitoring System Sensors

Table 7. Examples of the ReliaWind taxonomy

The full Wind turbine taxonomy enriched with reference numbers in order to make it more

easily used can be found in APPENDIX I.

5.2 Failure definition

Failure is defined to be the inability of a subassembly to perform its required function under

defined conditions; the item is then in a failed state, in contrast to an operational or working

state (Spinato, et al., 2009). Moving from the general definition to implementing a reliability

study limits should be customized in order to be precise on what will be considered a failure.In recent WTG reliability projects the limitations stated for a downtime event to be considered

a failure were (Wilkinson, 2011) :

The total duration of the event is 1 hour

Human intervention is required to set the turbine back operational state

Other researchers have tried to quantify the severity of a failure event according to the

duration of the failure event by dividing failures in minor (duration 1 day) and major

(duration > 1 day) considering that for a failure event of duration longer than 1 day the service

team will travel at least twice to the site (Faulstich, et al., 2010). An example with results of

failure separation in minor and major is shown in Figure 19.


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Figure 19. Reliability characteristics for different subassemblies in the WMEP programme dividing faults into minor

and major failures (Faulstich, et al., 2010)

For the purposes of this project the following criteria were set in order to consider a downtime

event as a failure:

The downtime event is due to a technical problem of the WTG, thus downtime

events due to environmental conditions (e.g. extreme wind or no low wind) or grid

problems are not counted as failures Human intervention is required to set the turbine back to operational state

The main difference compared to previous researchers, this project does not pose any duration

limit to the failure events, i.e. failure events of total downtime 1 hour that required a manual

restart are also taken into consideration. The reason for that is that failure events that require

only a manual restart and can possibly last less than 1 hour (for an onshore and easily

accessible wind farm) can be considered insignificant onshore but can cause long downtimes

offshore were accessibility is a major issue. Since MECALs O&M cost model is designed

also for offshore wind farms it was decided to include also these events. As a result, the

failure occurrence is expected to be higher compared to other WTG reliability research

projects.

For the division of the failures to severity sub-categories the initial logistic delay (i.e. the time

needed from the start of the failure event until the technician reaches the WTG) is ignored and

only the service time is taken into consideration. With these assumptions the failure events are

divided to 3 severity categories as shown in Table 8.


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Service Time

Manual Restart 1 hour

Minor Repair 1 hour Service Time 8 hours

Major Repair 8 hoursTable 8. Failure severity categories

The time limits were decided in cooperation with the WTG inspection team of MECAL

(Timessen & Links, 2013). 1 hour was considered as the time needed to perform a visual

check of the turbine and a manual restart without any repairing action. The division between

Minor and Major repair was made under the assumption that a repair that needs the technician

crew to be present for more than a working day (i.e. 8 hours) is a Major one. Any repairing

action that can be performed within a working day is considered a Minor repair.

The same titles for the severity categories are introduced in the ReliaWind project (Tavner,

2011, p. 13) as Maintenance Categories also with the presence of a fourth category called

Major Replacement based on the military standard for FMEC analysis MIL-STD-1629A

(U.S. Department of Defense, 1980) . The definition of the maintenance category in

Reliawind implies that detailed information concerning the maintenance activities wasavailable. Since the present research focuses on SCADA data an effort has been made to

quantify the limits for the severity categories. Additionally, the fourth category (Major

Replacement) defined in Reliawind requires maintenance activities information and is not

possible to be identified only through the analysis of SCADA data.

5.3 SCADA data processing algorithm

For this research project 10-minute SCADA data in combination with the relevant alarm logs

were processed in order to extract the failure events of operational wind farms and perform

further statistical analysis. More specifically, the following columns of the 10-minute SCADAdata were used:

SCADA counters

o Turbine OK counter

o Service On counter

o

Alarm counter

Timestamp

Power generated

Average wind speed

Additionally, the alarm logs available provide information about:

Code of the alarm initiating a failure

Starting time of the failure

For the data processing an algorithm was developed with the use of Visual Basic for

Applications and Microsoft Excel 2010. The algorithm processes the 10-min SCADA data, it

defines the failure events by combining the information with the alarm logs. The final result is

the processed information for the failure events in a given period of time divided according to


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their severity and the assembly where they occurred. A step-by-step description of the

algorithm is provided in the following sections.

5.3.1

Turbine State

Figure 20. Possible states of a WTG

Initially, the algorithm defines the state of the WTG according to the SCADA counters. The

SCADA counters are indications showing how many seconds the WTG was in each state

(Operational, Alarm and Service) in every 10-minute span (thus taking values from 0 to 600);a relevant example is provided in Table 9. The 3 possible states that a WTG can be in are:

Operational: When the WTG is generating or is capable of generating electricity. The

Turbine OK counter has values greater than 0 and the Alarm counter is 0.

Alarm: When a failure has occurred and the WTG cannot perform its function. The

Alarm counter has values greater than 0.

Service: When repairing or maintenance action takes place. The Service On counter

has values greater than 0.

The distinction between these different turbine states is mainly based on the SCADA counters

but there are some special cases that are treated in a different way. They can be summarized

as:

One of the alarm descriptions that appear when the WTG is in alarm state (according

to the SCADA counters) is Pause pressed on keyboard. In this case the algorithm

inserts a correction and the turbine is considered to be in service state since the

description indicates the presence of a technician in the turbine.

There are cases when the WTG appears to be operating normally according to the

SCADA counters (i.e. Turbine OK = 600), the wind speed is between cut-in and cut-

out but the WTG is not generating energy. In these cases, the turbine is considered to

be in Alarm state.


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Table 9. Example of turbine state definition according to SCADA counters

5.3.2 Short Running Periods

As Short Running Periods are defined the situations when the WTG is in alarm state for a

period of time, briefly operates again, returns to alarm state and eventually starts operating

normally again. After examining some of these cases and comparing them with the relevant

maintenance logs we concluded that the alarm periods that are interrupted by short running

periods in most of the cases belong to the same failure event. Thus, the short running periods

are ignored and the events (Alarm or Service) before and after are merged. The example in

Table 10 illustrates description above. The maximum duration of a short running is one hour.

Other authors mention similar events as Back-to-back events and consider them as separate

failure events (Peters, et al., 2012).

Table 10. Example of ignored short running period for a detected event

5.3.3 Event Indicators

As Event Indicators are defined the moments when the WTG changes from one state to

another. Depending on the initial and final state a different event indicator occurs. The eventindicators are defined as:

Service Start: The WTG state changes from Operational to Service

Service End: The WTG state changes from Service to Operational

Pause Start: The WTG state changes from Operational to Alarm

Pause End: The WTG state changes from Alarm to Operational

Pause End / Service Start: The WTG state changes from Alarm to Service

Wind Farm WTG # TimeStamp Turbine OK counter Service On Alarm Turbine State

2009-06-01 09:50 600 0 0 Operating

2009-06-01 10:00 600 0 0 Operating

2009-06-01 10:10 100 0 500 Alarm

2009-06-01 10:20 0 0 600 Alarm

2009-06-01 10:30 0 0 600 Alarm

2009-06-01 10:40 0 400 200 Service

2009-06-01 10:50 0 600 0 Service

SCADA Columns

Wind Farm WTG # TimeStamp Turbine OK counter Service On Alarm Turbine State Flag

2010-06-27 03:00 0 0 600 Alarm

2010-06-27 03:10 0 0 600 Alarm

2010-06-27 03:20 100 0 500 Alarm2010-06-27 03:30 600 0 0 Operational Short Run

2010-06-27 03:40 600 0 0 Operational Short Run

2010-06-27 03:50 249 0 351 Alarm

2010-06-27 04:00 0 0 600 Alarm

SCADA Columns


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In Figure 21 a schematic description of the event indicators is given. Following, an example

of the event indicators defined by the algorithm after data processing is shown in Table 11.

Figure 21. Event Indicators when the WTG state changes

Row # Project # Turbine ID TimeStamp Event Indicator

48489 6-02-12 13:00 Pause start

48491 6-02-12 13:20 Pause end

48717 8-02-12 3:00 Pause start

48722 8-02-12 14:30 Pause end / Service start48724 8-02-12 14:50 Service end

51361 26-02-12 22:30 Pause start

51363 26-02-12 22:50 Pause end

51907 1-03-12 17:50 Pause start

51909 1-03-12 18:10 Pause endTable 11. Example of event indicators

In the scope of this research the total downtime is divided into Alarm duration and Service

duration. As alarm duration is defined the initial logistic delay time, i.e. the time needed

from the moment the WTG stops until the first human intervention (Service state). When the

WTG state changes to Service then the assumption that the repairing action lasts until theturbine starts operating normally again is made. For this reason, there is no event indicator for

the state change from Service to Alarm state. Even though Service to Alarm can be observed,

this does not indicate that the service action is terminated. An example is shown in Table 12.

Making this assumption information concerning logistic delay after the first service action

(e.g. waiting time for spare parts, technicians unavailability) cannot be distinguished. Thus,

the actual repairing time is possibly shorter than what is estimated as Service duration.


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Table 12. Example of Repairing action

5.3.4 Downtime Events

The purpose of defining the event indicators was to be able to distinguish and categorize the

downtime events that occurred during the period the SCADA data of which is examined. The

categorization of the downtime events by the algorithm developed is presented in Figure 22.

The different types of downtime events are defined according to the event indicators as

following:

Failure (Pause Start Pause End / Service Start Service End): A failure event startswhen the turbine state changes to Alarm (Pause Start) followed by a downtime period

Wind Farm WTG # TimeStamp Turbine OK counter Service On Alarm Turbine State Flag

2011-12-30 19:50 600 0 0 Operational

2011-12-30 20:00 146 0 454 Alarm

2011-12-30 20:10 0 0 600 Alarm2011-12-30 20:20 0 0 600 Alarm

2011-12-30 20:30 0 0 600 Alarm

2011-12-30 20:40 0 102 498 Service Repairing




2011-12-30 21:20 0 0 600 Alarm Repairing

2011-12-30 21:30 0 0 600 Alarm Repairing




SCADA Columns

Figure 22. Downtime event categorization


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until human intervention is detected (Pause End / Service Start) and ending when the

WTG starts operating again.

o

The duration between Pause Start and Pause End / Service Start is defined as

Alarm duration

o The duration between Pause End / Service Start and Service is defined as

Service duration

o

The distinction of the failure events in Manual Restart, Minor Repair & MajorRepair is given in Table 8.

Auto-Restart (Pause Start Pause End): Downtime event which is solved by the

WTG itself or with a remote restart, without the natural presence of a technician

needed.

Scheduled Service (Service Start Service End): Downtime event during which the

turbine was in service state without any alarm

The Automatic Restarts and Scheduled Services are not counted as failures and will not be

part of the statistical analysis that will follow. Though, they can be used for further research

topics (e.g. Maintenance planning, Connection between the frequency of automatic restartsand failure events).

5.3.5 Alarm Logs

The 10-minute SCADA data are used as described in the previous sections of this chapter in

order to define the failure events and categorize them according to their duration. Alarm logs

contain additional information concerning the kind of the failures through the alarm numbers

and descriptions provided by WTG Control and Communication System. The columns used

from the Alarm log are:

Event Detected Timestamp Error number

Error description

In order to connect the information extracted from the 10-minute SCADA data with the alarm

logs some data modifications were needed. The timestamp of the alarm log is given in

accuracy of 1 minute while the SCADA has 10-minutes accuracy. For this reason, the alarm

log timestamp was rounded-up to the next 10 minutes timestamp they have different

frequency measurements. For each failure event the alarm that initiated the event is

considered responsible for the failure, which means that the assembly from which the WTG

Control system received a signal is considered the one that have failed. As pointed out also by

other researchers, the fact that a failure occurred in a component does not necessarily meanthat the component itself is responsible for the failure (Tavner, et al., 2007). Further research

that exceeds the scope of this project would be needed to identify the root cause of each

failure.

There have been cases that the 10-minute time span when a failure event occurred did not

agree with the alarm log indication. In these cases the previous and next 10-minutes span was

examined. If there still was no match the failure event defined by the SCADA data processing

was marked as Unknown.


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An overview figure of the algorithm developed and described in this chapter is provided in the

following Figure 23.


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Figure 23. Overview of the process


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6 Failure statistical analysis

The extraction of failure information from SCADA data, as developed in the previous chapter,

is followed by the statistical analysis of the results. Initially, overall cumulative results

concerning the failure occurrence and downtime for each turbine assembly are presented in

this chapter. Following, the different statistical methodologies used depending on the failure

type are described.

6.1

Overall results

In this section the overall results of the SCADA data analysis of the two wind farms that were

chosen as test case are presented. In Figure 24 and the relevant Table 14 the normalized

failure frequency is presented. Additionally, in Figure 25 and Table 15 the normalized

downtime for each wind turbine assembly is also shown.

Figure 24. Results - Normalized failure frequency for wind turbine assemblies (including only identified failures)

000%

002%

004%

006%

008%

010%

012%

Normalized failure events

Manual Restart

Minor Repair

Major Repair


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WTG Assemblies Manual Restart

Minor

Repair

Major

Repair

Grand

Total

Auxiliary Electrical System 1,29% 1,43% 0,43% 3,15%

Blade 0,14% 0,00% 0,00% 0,14%

Control and Communication System 3,43% 5,01% 1,29% 9,73%

Frequency Converter 8,01% 5,15% 2,43% 15,59%

Gearbox 2,29% 4,15% 0,57% 7,01%

Generator 5,01% 2,72% 0,86% 8,58%

Grid Connection 6,01% 3,00% 0,57% 9,59%

Hydraulics System 1,00% 2,00% 0,29% 3,29%

Main Shaft Set 0,00% 0,14% 0,00% 0,14%

Nacelle Auxiliaries 0,14% 0,14% 0,14% 0,43%

Pitch System 7,73% 9,73% 2,15% 19,60%

Power Electrical System 0,14% 0,43% 0,43% 1,00%

Tower 6,44% 0,57% 0,29% 7,30%Unknown 5,87% 3,72% 1,57% 11,16%

Yaw System 1,57% 1,29% 0,43% 3,29%

Grand Total 49,07% 39,48% 11,44% 100,00%Table 14. Results - Normalized failure frequency (over the total number of failure events)

From the results in Figure 24 the following observations can be made:

The wind turbine assemblies which appear to have higher failure frequency are

o Pitch system (19.6%)

o Frequency converter (15.59%)

o

Control & Communication system (9.73%)

The frequency of the event types is higher for less severe events with Manual restartsrepresenting almost half of the events (49.07%). Though it should be mentioned that

for several assemblies the occurrence of Minor restarts is higher compared to Manual

restarts.

From the wind turbine reliability previous research projects (as presented in chapter 3)

it would be more reasonable to compare the results with those from the Reliawind

project because the wind turbines used are of rated power >850kW as well as those

examined in the current research project. In the results of Reliawind the assemblies

with the highest failure frequency are (see Error! Not a valid bookmark self-

reference.):

o

Pitch system (21.29%)o Frequency converter (12.96%)

o Yaw system (11.28%)

The results agree with those of Reliawind that the two most critical assemblies are the

Pitch system and the Frequency converter. The Yaw system failures in the current

research are lower compared to the results of Reliawind.

There is a significant percentage of failure events that were not identified (11.16%)

(see Table 14). This can be because of unclear or ambiguous alarm description or no


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alarm at all in the Alarm log. In these cases, additional O&M information (e.g.

Maintenance logs) would be useful.

Figure 25. Normalized downtime for wind turbine assemblies (including only identified failures)

Row Labels Manual Restart

Minor

Repair

Major

Repair

Grand

Total

Auxiliary Electrical System 0,36% 0,32% 2,33% 3,01%

Blade 0,01% 0,00% 0,00% 0,01%

Control and Communication System 0,54% 1,73% 5,73% 8,00%

Frequency Converter 1,75% 1,72% 13,76% 17,23%

Gearbox 1,06% 1,82% 3,34% 6,22%

Generator 0,87% 0,91% 0,72% 2,50%

Grid Connection 0,97% 0,67% 3,48% 5,11%

Hydraulics System 0,31% 4,07% 0,19% 4,57%

Main Shaft Set 0,00% 0,16% 0,00% 0,16%

Nacelle Auxiliaries 0,10% 0,03% 0,59% 0,72%Pitch System 0,89% 3,24% 7,84% 11,97%

Power Electrical System 0,07% 0,28% 8,63% 8,99%

Tower 2,17% 0,19% 3,59% 5,95%

Unknown 1,33% 1,00% 21,15% 23,48%

Yaw System 0,28% 0,51% 1,31% 2,10%

Grand Total 10,70% 16,64% 72,65% 100,00%

000%

002%

004%

006%

008%

010%

012%

014%

016%

018%

020%

Downtime

Manual Restart

Minor Repair

Major Repair


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Table 15. Normalized downtime for wind turbine assemblies (over the total number of failure events)

From the results in Figure 25 the following observations can be made:

The wind turbine assemblies that cause the longest downtime are:

o Frequency converter (17.23%)

o Pitch system (11.97%)

o

Power electrical system (8.99%)

As expected by the event type definition the contribution of the major repairs to thetotal downtime is significantly larger compared to the other two categories.

Accordingly, the total downtime for minor repairs is longer than the total downtime

for manual restarts despite the higher frequency of manual restarts.

The frequency converter failures have a higher contribution to the total downtime

(17.23%) compared to their contribution to the total number of failures (12.96%). The

opposite appears for the pitch system; the pitch system has lower contribution to the

total downtime (11.97%) compared to its contribution to the total number of failures

(19.6%). In general, the contribution of the assemblies to the total number of failurescompared to their contribution to the total downtime does not show large deviation.

An exception is the power electrical system with only 1% of the total number of

failures but 8.99% of the total downtime. Though, it should be taken into

consideration that the turbine population used for this research is relatively small, thus

the results can be affected by specific incidents.

The percentage of the total downtime due to unidentified failure events is significant

(23.48%). This high percentage appears because of very long (unidentified) downtime

events (with duration longer than 30 days) in few turbines of one of the wind farms

As mentioned before, additional sources of O&M information can identify more of the

unknown events and decrease this percentage.

In some of the previous research on wind turbine reliability it was observed that the

gearbox and generator had a much higher contribution to the total downtime compared

to the percentage of failures occurred (Figure 12, Figure 14, Figure 16). This does not

occur in the turbine population examined in this project. In general, major failures in

gearboxes and generators have long downtimes because the replacement of these

assemblies (if needed) is a long operation with possibly long logistic delay time

(special equipment, weather restrictions). Though, the relatively short period of the

data used for this project indicates that no major replacements of these assemblies took

place during this period.


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6.2 Statistical analysis

6.2.1 Recent research

In chapter 3 an overview of the research projects on wind turbine reliability carried out in the

past was made. The initial efforts focused on counting the failure occurrence and extractingreliability results in terms of failure rates (failures per wind turbine assembly per year). This

was mainly based on the assumption that the failure rate of wind turbine assemblies follows

the bathtub curve shape (Figure 26), i.e. having a constant failure rate during the useful life

period.

Figure 26. The bathtub curve shape of failure rate over time for a system's lifetime, (Rausand & Hyland, 2004)

In more recent research projects efforts have been made to examine the evolution of the

failure rate during the lifetime of a wind turbine. Spinato et al. used the Power Law process tomodel the reliability growth of Danish and German wind turbines but also the reliability

growth of several wind turbine assemblies (Spinato, et al., 2009). The reliability growth plot

for the German Danish wind turbines was shown in Figure 18. Additionally, Andrawus used

the Weibull distribution to model the failures of operational wind farms examined (Andrawus,

2008). An example of failure rate plot modelled with the 2-parameter Weibull distribution is

shown in Figure 27. Moreover, recent findings from failure data of 2 operational wind farms

have demonstrated failure behaviour different than the bathtub curve. The author of that

report expresses his doubts about the assumption of constant failure rate (Buckley, 2013). The

failure frequency for the two wind farms used in the research of Buckley is shown in Figure

28.


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Figure 27. Main shaft set failure rate plot, (Andrawus, 2008)

Figure 28. Failure occurrence for 2 operational wind farms, (Buckley, 2013)

6.2.2 Methodology proposal

For the statistical analysis of the failure events extracted with the use of the SCADA data

processing algorithm, depending on the event type the following methods were selected:

Power Law Process model for the Manual restarts & Minor repairs

Weibull distribution for the Major repairs

6.2.2.1

Manual restarts

Manual restarts of a wind turbine as defined in chapter 2 are failure events that require the

presence of the technical crew but no repairing action takes place. The only intervention is

rebooting the turbine controller. It cannot be considered that the condition of the turbine has

improved after a manual restart and thus using a distribution would not be appropriate for

modelling manual restarts. As thoroughly explained in chapter 2 a distribution can be used to


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model a single lifetime, consequently using a distribution indicates that the system is as-

good-as-new after the failure (Crow, 2004). For modelling inter-occurrence of manual

restarts the Power Law Process (PLP) model is used under the assumption that the system is

as-bad-as-old after the failure.

For the estimation of the parameters & of the PLP model a simple VBA algorithm was

created to solve (numerically) the system of equations presented in section 2.2.2.2:

=

( )

=

[ ] ()

The results of the parameter estimator were verified with the use of the reliability

software Reliasoft RGA 9 (trial version) (Reliasoft, 2013).

Since there is no actual repairing during a manual restart there is no reason to group

the manual restarts to the wind turbine assemblies. All the manual restarts for each

wind farm are grouped together for the estimation of the parameters of the PLP. The

results are presented in

Wind farm Turbine * Days Beta () Lamda ()

A 21712 1,1136 0,0048

B 25381 1,0323 0,0053

Table 16and the relevant reliability plots in Figure 29.


A 21712 1,1136 0,0048

B 25381 1,0323 0,0053Table 16. PLP model parameters for Manual Restarts


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Figure 29. Failure rate function plot, Manual Restarts

6.2.2.2

Minor Repairs

The same methodology as explained for manual restarts is applied also for minor repairs.

During minor repairs service action takes place repairing or replacing components of the wind

turbine assembly. As described in the wind turbine taxonomy (section 5.1), an assembly can

be considered a system consisted of several sub-assemblies and components. Consequently, aminor repairing action or a replacement of a component is still considered that brings the

system back to operating state in the condition it was before the failure.

Because of the relatively small turbine population and time length of the sample used for thisproject the minor repairs for each wind farm are grouped together in order to estimate the

parameters of the PLP model. Though, it is suggested to model the minor repairs of each

assembly separately if a larger dataset is available. From the current dataset the PLP model

was applied to the minor repairs of the pitch system which was the most critical assembly

(and thus the amount of minor repairs was sufficient for the analysis). The results for the

minor repairs in the two wind farms under discussion are presented in Table 17 and the

relevant reliability growth plots in Figure 30and for the pitch system in Table 18 and Figure

31.


A 21712 1,0522 0,0090B 25381 1,1829 0,0017

Table 17. PLP model parameters for Minor Repairs

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0 200 400 600 800 1000

Failurerate(Failure

s/day)

Time (days)

Manual Restarts

Wind Farm A

Wind Farm B


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Figure 30. Failure rate function plot, Minor Repairs


A 21712 0,9463 0,0053

B 25381 0,8483 0,0015Table 18. PLP model parameters, Minor repair - Pitch system

Figure 31. Failure rate function plot, Minor Repair - Pitch system

0

0,002

0,004

0,006

0,0080,01

0,012

0,014

0,016

0 200 400 600 800 1000

Failure

Rate

(failu

res/day)

Time (days)

Minor Repairs

Wind Farm A

Wind Farm B

0

0,001

0,002

0,003

0,004

0,005

0,006

0 200 400 600 800 1000

Failurerate(failures/day)

Time (days)

Minor Repair - Pitch system

Wind Farm A

Wind Farm B


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6.2.2.3 Major Repairs

For the major repairs the assumption that the assembly is as-good-as-new after the repairing

action is considered realistic to be made. In this case there is no inter-occurrence of failure

events but a new lifetime of the assembly starts after each major repair. Thus, the major

repairs can be modeled with the Weibull distribution.

For the estimation of the Weibull shape and scale parameters (,) Dr. Bobs Reliability

Calculator, an Excel-based tool developed by NASA was used (NASA , 2013). Because of

the relatively small turbine population and time length of the sample used for this project only

major failures of assemblies for which there was sufficient number of events. Especially for

wind farm B for which the dataset was shorter no more than 2 major failures were detected for

the same assembly. Thus, it was considered of little statistical value to perform Weibull

analysis in such a small sample. The weibull distributions of the pitch system and the

frequency converter for wind farm A are presented in Figure 32 & Figure 33.

Figure 32. Weibull plot for major failures of Frequency converters of wind farm A

.01 .1 1 10 100 1000 10000 100000

.01

.05

.1

.5

1

5

10

5063.2

9095

9999.9

99.99

1 3 1

7

11

CumulativeOccurrence(%)

Time (days)

Frequency Converter

Weibull


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Figure 33. Weibull plot for major failures of Pitch system of wind farm A

.001 .01 .1 1 10 100 1000 10000 100000

.01

.05

.1

.5

1

5

10

5063.2

9095

99

99.999.99

1

3

1

5

13

CumulativeOccurrence(%)

Time (days)

Pitch System

Weibull


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7 Prediction model

Recent research has been carried out in order to define the impact of environmental factors

on WTG reliability. In this chapter an overview of the findings of this research is provided.

Additionally, an effort to adapt the results of the statistical analysis performed in the previous

chapter

Documents

Christos Kaidis