Ain Shams University

Faculty of Engineering

Electrical power & Machines Department

Adaptive Load Shedding for Stand-Alone Power

Systems

M.Sc. Thesis

By

Eng. Taghreed Ibrahim Abdel Hady Shaarawy

Submitted in partial fulfillment of the requirements for the M.Sc. degree

in Electrical Engineering

Supervised By

Prof. Dr. Hossam El Din Abd Allah Talaat

Dr. Rania Abdel Wahid Abdel Halim

Electrical Power and Machines Department,

Faculty of Engineering, Ain Shams University

Cairo, 2017

Examiners Committee

For the Thesis

Adaptive Load Shedding for Stand-Alone Power Systems

By

Eng. Taghreed Ibrahim Abdel hady Shaarawy

A thesis Submitted to the Faculty of Engineering Ain Shams University

in partial fulfillment of the requirements for the M.Sc. Degree in

Electrical Power and Machine Engineering

Name, title and affiliation Signature

1. Prof. Dr. Hanafy Mahmoud Ismail

Electrical Power and Machines Eng. Department,

Faculty of Engineering, Ain Shams University.

2. Prof. Dr. Rania Metwally Awad Elsharkawy

Head of Electrical and control Eng. Department,

Arab Academy for Science and Technology

and Maritime Transport, Cairo.

3. Prof. Dr. Hossam El Din Abdallah Talaat

Electrical Power and Machines Eng. Department,

Faculty of Engineering, Ain Shams University

Supervisors Committee

For the Thesis

Adaptive Load Shedding for Stand-Alone Power Systems

By

Eng. Taghreed Ibrahim Abdel hady Shaarawy

A thesis Submitted to the Faculty of Engineering Ain Shams University

in partial fulfillment of the requirements for the M.Sc. Degree in

Electrical Power and Machine Engineering

Approved by:

Name, title and affiliation Signature

1. Prof. Dr. Hossam El Din Abdallah Talaat

Electrical Power and Machines Department,

Faculty of Engineering, Ain Shams University

2. Dr. Rania Abdel Wahid Abdel Halim

Electrical Power and Machines Department,

Faculty of Engineering, Ain Shams University

Statement

ii

STATEMENT

This Thesis is submitted to Ain Shams University in partial fulfillment

of the requirements of M.Sc. degree in Electrical Engineering.

The included work in this thesis has been carried out by the author at the

department of electrical power and machines, Ain Shams University. No

part of this thesis has been submitted for a degree or a qualification at

any other university or institution.

Name: Taghreed Ibrahim Abdel Hady Shaarawy

Signature:……………………………………….

Date: / /

ACKNOWLEDGEMENT

iii

ACKNOWLEDGEMENT

I would like to express my sincere gratitude to Professor. Dr. Hossam

El Din Abd Allah Talaat for giving me the opportunity to work under

his supervision, for the continuous support of my study and research, for

his patience, motivation, immense knowledge, and teaching me how to

think. His guidance helped me in all the time of research and writing of

this thesis.

I wish to express my deep thanks to Dr. Rania Abdel Wahid Abdel

Halim for her supervision, supporting, helpful advice, continuous

guidance, theoretical advice and assistances during the stages of

preparation of this work.

Finally, I am grateful for my parents and my husband, who helped me

throughout all of this work. Thank you for supporting me in every way.

Taghreed Ibrahim Abdel Hady Shaarawy

Cairo, 2017

Abstract

iv

ABSTRACT

Load frequency control is commonly used to maintain power

system frequency very close to its nominal value. A small

deviation from the rated frequency may have harmful impact on

the components of the system. Under-Frequency Load Shedding

(UFLS) is to be used when the regular frequency control loops fail

to restore the frequency to its rated value. In such conditions, the

frequency continues to fall until adequate loads are disconnected

from the network. Usually, renewable energy generation in

microgrids represents a considerable percentage of the total

generation. Therefore, application of UFLS in microgrids is crucial

since they are exposed to rapid deficiency in generation due to

weather conditions.

There are two main categories of UFLS: conventional and adaptive

load shedding. Conventional UFLS has a fixed number of stages

and fixed time delays while the adaptive UFLS applies shedding,

in most cases, in one stage in the shortest possible delay.

This work introduces two adaptive UFLS schemes for an islanded

microgrid both are based on artificial intelligence (AI). These

schemes rely on estimating the value of load to be shed in one

stage. As a base of comparison, a heuristic methodology for load

shedding has been used. This heuristic load shedding is applied by

creating a look up table using the loads history and willingness to

pay.

The under-frequency load shedding schemes developed in this

work has utilized two well-known AI techniques, namely fuzzy

logic and artificial neural network. Both schemes have two inputs

and one output. The inputs are the maximum deviation in

frequency and the absolute value of the rate of change of frequency

Abstract

v

at the instant of islanding. The output of the proposed scheme is

the amount of load to be shed to maintain the frequency of the

power system within the acceptable limits. Training patterns

obtained from the simulated system are used to tune both the fuzzy

rule base and the neural network so as to give accurate results.

Fifteen simulation case studies have been used for this purpose.

Testing stage have been implemented using additional 15

simulation case studies.

The obtained results prove the effectiveness of the developed

scheme in estimating the amount of the load to be shed to restore

the system frequency back near to its nominal value. The main

advantage of the proposed scheme is its capability in performing

the load shedding in one stage with minimum load disconnection

which enhances the system reliability and improves the transient

response of the frequency deviation of the system.

Keywords: Distributed generation, Renewable sources, Speed

governor, Underfrequency load shedding, Stand-alone power

system, Fuzzy logic, Artificial Neural Network.

Table of Contents

vi

TABLE OF CONTENTS

ABSTRACT iv-v

TABLE OF CONTENTS vi-viii

LIST OF TABLES ix

LIST OF FIGURES x-xii

LIST OF ABBREVIATIONS xiii

1. INTRODUCTION

1.1. General …………………………………………………………..

1.2. Objectives of the thesis ………………………………………….

1.3. Thesis Outlines ………………………………………………….

1-3

1-2

2

2-3

2. THEORETICAL BACKGROUND AND LITERATURE

REVIEW

2.1. General …………………………………………………………..

2.2. Distributed Generation (DG) …………………………………….

2.2.1. DG Applications ……………………………………………...

2.2.2. Types of Distributed Generators ……………………………...

2.2.2.1. Traditional Combustion Generators: Micro-turbine

(MT) …………………………………………………...

2.2.2.2. Non-Traditional Combustion Generators ……………...

A. Electromechanical Devices: Fuel cell

B. Storage Devices …………………………………….

C. Renewable Devices …………………………………

2.2.3. Types of Distributed Generators ……………………………...

2.2.4. Advantages of distributed generation ………………………...

2.2.5. Disadvantages of distributed generations …………………….

2.3. Review of Methods for Islanding Detection Techniques ………..

2.4. Effect of Increasing Renewable Sources in Power System ……..

2.5. Frequency Regulation in Power Systems ………………………..

2.5.1. The need for frequency regulation ………………………..

A. Impact of frequency variation on steam turbines ……...

B. Impact of under frequency variations on Generators ….

C. Impact on other network components …………………

2.5.2. Role of load frequency control ……………………………

4-27

4

4-9

4-5

5-9

5

6

6

6

6

6-7

8

8-9

9-10

10-13

14-18

14-15

14-15

15

15

16

Table of Contents

vii

2.5.3. Evaluation of frequency performance in typical power

networks …………………………………………………..

2.6. Underfrequency Load Shedding Schemes ………………………

2.6.1. Industrial Underfrequency Load shedding schemes………..

2.6.2. Review of Underfrequency load shedding techniques……..

16-18

19-27

19-21

21-27

3. SIMULATION OF HEURISTIC UFLS SCHEME

3.1. General …………………………………………………………

3.2. Problem Description …………………………………………...

3.3. Role of speed governor and its limits ………………………….

3.3.1. Role of speed governor …………………………………….

3.3.2. Limits of speed governor control …………………………..

3.4. System Understudy Single Line Diagram ……………………..

3.5. Simulink Model ………………………………………………..

3.6. Application of Heuristic Scheme ………………………………

3.6.1. Procedure for creating the look up table ………………….

3.6.2. Algorithm of Underfrequency load shedding …………….

3.7. Simulation Results ……………………………………………..

3.7.1. Case study (I) ……………………………………………..

3.7.2. Case study (II) ……………………………………………

28-49

28

28-31

32-34

32

32-34

34

34-36

37-40

37-38

38-40

40-49

40-43

43-49

4. ARTIFICIAL INTELLIGENT-BASED UFLS SCHEMES

4.1. General …………………………………………………………..

4.2. Fuzzy Logic Fundamentals ……………………………………...

4.3. Proposed Methodology ………………………………………….

4.4. Generation of case studies ………………………………………

4.5. Design steps …………………………………………………….

4.5.1. Range of variables ……………………………………..

4.5.2. Membership functions …………………………………

4.5.3. Rule-Base ……………………………………………...

4.6. Considerations of On-Line Application of FL-UFLS Scheme ….

4.7. Results of Fuzzy UFLS scheme …………………………………

4.7.1. Case study (7) ………………………………………...

4.7.2. Case study (10) ……………………………………….

4.7.3. Summary of results …………………………………..

4.8. ANN Fundamentals ……………………………………………..

4.9. Methodology of ANN- UFLS …………………………………..

50-84

50

50

50-51

51-52

52-55

53

53-54

54-55

55-56

56-60

57

58

59-60

60-61

61-62

Table of Contents

viii

4.10. Design of ANN-UFLS Scheme ……………………………

4.11. Results of ANN-UFLS Scheme ……………………………

4.12. Comparison between Developed UFLS Schemes …………

4.12.1. Comparison between Heuristic and FL-UFLS ……..

4.12.1.1. Effect of the location of the load to be shed …….

4.12.1.2. Comparison between Heuristic and FL-UFLS ….

A. Performance of Heuristic and FL-UFLS for

PDrated= 40 MW …………………………………

B. Performance of Heuristic and FL-UFLS for

PDrated= 43 MW ………………………………….

4.12.2. Comparison between fuzzy and ANN techniques …..

4.12.2.1. Simulation results ……………………………...

4.12.2.2. Summary of results …………………………….

62-64

64-73

74-84

74-79

74-75

75-79

76-77

77-79

79-84

80-83

83-84

5. CONCLUSIONS 85-86

REFERENCES 87-89

LIST OF PUBLICATION 90

LIST OF TABLES

ix

LIST OF TABLES

Table 2.1 Types of DGs and their capacity 7

Table 2.2 WECC Load shedding stages 20

Table 2.3 Additional Load shedding stages 21

Table 2.4 Seven load shedding steps in the Egyptian power

system 22

Table 3. 1 Example of the look up table 38

Table 3. 2 Look up table for case study I 42

Table 3. 3 Look up table for case study II scenario 1 45

Table 3. 4 Look up table for case study II scenario 2 48-49

Table 4.1 Summary of simulation of 15 case studies 52

Table 4.2 Developed Output Fuzzy Rule Base 55

Table 4.3 Summary of results 59-60

Table 4.4 Testing case studies 64

Table 4.5 Summary of Testing Results 65

Table 4.6 Performance Evaluation of the 4 UFLS Schemes 70

Table 4.7 Summary of Testing Results for ANN31, ANN32,

ANN33 and ANN34 72

Table 4.8 Performance Evaluation of the 4 UFLS Schemes 73

Table 4.9 Effect of location on maximum overshoot and steady

state frequency for fuzzy scheme 74

Table 4.10 Effect of location on maximum overshoot and steady

state frequency for heuristic scheme 75

Table 4.11 Comparison between heuristic and FL-UFLS by

shedding loads from same location 75

Table 4.12 Estimated load shed (p.u.) for the best ANN scheme

and the FL scheme 80

Table 4.13 Estimated Pshed for FL-UFLS and ANN3-UFLS

versus target shedding load 83

Table 4.14 Performance Evaluation of the 2 UFLS Schemes 84

LIST OF FIGURES

x

LIST OF FIGURES

Figure 2.1 DG Technologies 5

Figure 2.2 Islanding detection techniques 9

Figure 2.3 Global Installed wind power capacity (MW) 11

Figure 2.4 Global Wind Power Cumulative Installed Capacity 12

Figure 2.5 IEEE C37-106 Worst case frequency withstand 15

Figure 2.6 Average frequency values in Continental Europe, June

2003 and June 2010 for Swiss grid

17

Figure 2.7 Frequency quality behaviour in Continental Europe

during the last ten years in Swiss grid 18

Figure 2.8 NERC Areas 20

Figure 3.1 Micro grid connected to main grid 28

Figure 3.2 Frequency dip within the European power system 29

Figure 3.3 Frequency variation with different power deficiency 31

Figure 3.4 Frequency deviation with speed governor control 33

Figure 3.5 Frequency deviation for islanding condition 34

Figure 3.6 Single line diagram 35

Figure 3.7 Simulink model 36

Figure 3.8 Algorithm for heuristic underfrequency load shedding 39

Figure 3.9 loads configurations for case study I 41

Figure 3.10 Frequency variation with time for case study (I) 43

Figure 3.11 loads configurations for case study II scenario 1 44

Figure 3.12 Frequency variation with time for case study (II)

scenario 1 46

Figure 3.13 loads configurations for case study II scenario 2 47

Figure 3.14 Frequency variation with time for case study (II)

scenario 49

LIST OF FIGURES

xi

Figure 4.1 Proposed FL-UFLS Scheme 51

Figure 4.2 Membership function of input 1 53

Figure 4.3 Membership function of input 2 54

Figure 4.4 Membership function of the output 54

Figure 4.5 algorithm of FL-UFLS scheme 56

Figure 4.6 Frequency variation with time for case study (7) 57

Figure 4.7 Frequency variation with time for case study (10) 58

Figure 4.8 General Architecture of the ANN scheme 61

Figure 4.9 Architecture of the ANN scheme 61

Figure 4.10 Methodology of ANN-based UFLS scheme 62

Figure 4.11 Internal Architecture of the ANN 63

Figure 4.12 ANN1-UFLS Frequency Variation for PDrated = 38.5

MW 66

Figure 4.13 ANN2-UFLS Frequency Variation for PDrated = 38.5

MW 66

Figure 4.14 ANN3-UFLS Frequency Variation for PDrated = 38.5

MW 67

Figure 4.15 ANN4-UFLS Frequency Variation for PDrated = 38.5

MW 67

Figure 4.16 ANN1-UFLS Frequency Variation for PDrated = 43.5

MW 68

Figure 4.17 ANN2-UFLS Frequency Variation for PDrated = 43.5

MW 68

Figure 4.18 ANN3-UFLS Frequency Variation for PDrated = 43.5

MW 69

Figure 4.19 ANN4-UFLS Frequency Variation for PDrated = 43.5

MW 69

Figure 4.20 Bar chart for ANN1, ANN2, ANN3 and ANN4

comparing mean error and rms 71

LIST OF FIGURES

xii

Figure 4.21 Bar chart for ANN31, ANN32, ANN33 and ANN34

comparing mean error and rms 73

Figure 4.22 FL-UFLS Frequency Variation for PDrated = 40 MW 76

Figure 4.23 Heuristic Frequency Variation for PDrated = 40 MW 77

Figure 4.24 FL-UFLS Frequency Variation for PDrated = 43 MW 78

Figure 4.25 Heuristic scheme – Scenario 1, Frequency Variation

for PDrated = 43MW 78

Figure 4.26 Heuristic scheme – Scenario 2, Frequency Variation

for PDrated = 43MW 79

Figure 4.27 FL-UFLS Scheme Frequency Variation for

PDrated=38.5 MW 81

Figure 4.28 ANN34-UFLS Scheme Frequency Variation for

PDrated=38.5 MW 81

Figure 4.29 FL-UFLS Scheme Frequency Variation for PDrated =

47.5 MW 82

Figure 4.30 ANN34-UFLS Scheme Frequency Variation for

PDrated = 47.5 MW 82

Figure 4.31 Bar chart for ANN34 and fuzzy comparing mean error

and rms 84

LIST OF ABBREVIATIONS

xiii

LIST OF ABBREVIATIONS

DG Distributed Generators

WECC Western Electricity Coordinated Council

AI Artificial Intelligent

UFLS Under-Frequency Load Shedding

ANN Artificial Neural Network

NERC North American Electric Reliability Council

PLL Phase Locked Loop

FLLSC Fuzzy Logic Load Shedding Controller

LSCM Load Shedding Controller Module

ROCOF Rate Of Change Of Frequency

ROCOFLi Rate Of Change Of Frequency for load i

FL Fuzzy Logic