UNIVERSITI PUTRA MALAYSIA RELIABILITY ASSESSMENT OF …

UNIVERSITI PUTRA MALAYSIA

RELIABILITY ASSESSMENT OF POWER SYSTEM GENERATION ADEQUACY WITH WIND POWER USING POPULATION-BASED

INTELLIGENT SEARCH METHODS

ATHRAA ALI KADHEM

FK 2018 23

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UPMRELIABILITY ASSESSMENT OF POWER SYSTEM GENERATION

ADEQUACY WITH WIND POWER USING POPULATION-BASED INTELLIGENT SEARCH METHODS

By

ATHRAA ALI KADHEM

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfillment of the Requirements for the Degree of Doctor of Philosophy

��2017

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COPYRIGHT

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Malaysia.

Copyright © Universiti Putra Malaysia

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DEDICATION

To my Parents,

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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment

of the requirement for the degree of Doctor of Philosophy

RELIABILITY ASSESSMENT OF POWER SYSTEM GENERATION ADEQUACY WITH WIND POWER USING POPULATION-BASED

INTELLIGENT SEARCH METHODS

By

ATHRAA ALI KADHEM

�� 2017

Chairman : Associate Professor Noor Izzri Bin Abdul Wahab, PhD Faculty : Engineering

Reliability of the generation system is an important aspect of planning for the

expansion of future system capacity which ensures that the total installed capacity

would be sufficient to provide adequate electricity, therefore, it is necessary to

investigate the reliability of the power system. The reliability assessment of the

adequacy of the generating system is normally calculated by using either analytical or

simulation methods. The Monte Carlo simulation (MCS) method enables an accurate

evaluation of reliability indices. The drawback of MCS is that it is not suitable for a

system with large number of components in a system or high-reliability performance

that require large computational effort which would take a long time to converge

efficiently. This study sought to examine the performance of three newly proposed

techniques, for reliability assessment of the power systems, namely Disparity

Evolution Genetic Algorithm (DEGA), Binary Particle Swarm Optimisation (BPSO),

and Differential Evolution Optimization Algorithm (DEOA). The proposed intelligent

algorithms would rely on the population intelligent search (PIS) techniques considered

as viable replacement for the MCS method in assessing the reliability indices of non-

chronological system. The advantage of using these algorithms is obvious as they

would speed up the computation to obtain higher accuracy with less computation

effort. In recent years, the development of wind power to meet the demand for

electricity has received considerable attention. However, this energy source differs

considerably from the conventional generation sources because it is intermittent in

nature and may lead to high-risk levels in the electrical system reliability. As such, the

three novel PIS techniques (i.e., DEGA, DEOA, and BPSO) were proposed for

reliability assessment of power generation systems with the integration of wind

energy. These methods proved accurate in estimating the reliability indices, with less

computation effort. In this study, analysis was made on the wind speed data

characteristics and wind power potential assessment at three a given sites in Malaysia

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namely Mersing, Kudat, and Kuala Terengganu. Results have shown that Mersing and

Kudat were suitable as wind sites. The findings of this research have provided

evidence to support those of the previous studies were conducted separately for

Mersing and Kudat indicating that these sites could potentially be utilised to construct

a new wind power plant in Malaysia. Additionally, the present study has developed a

prediction model of wind speed for these three sites in Malaysia. This model took into

consideration the seasonal wind speed variation during the year, in the format of the

combined method comprising the Weibull model with artificial neural network

(ANN), so that the forecasting errors of wind values would be lower than those

generated by using only the Weibull model. It was suggested that the wind power

should be connected to the Roy Billinton Test System (RBTS), from two sites in

Malaysia. The reliability indices obtained before and after the inclusion of the two

farms to the system under consideration were compared. Based on this analysis, it was

found that, with the inclusion of wind power from both sites, the reliability indices had

slightly improved the reliability of RBTS.

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Doktor Falsafah

PENILAIAN KEBOLEHPERCAYAAN KECUKUPAN PENJANAAN SISTEM KUASA DENGAN KUASA ANGIN MENGGUNAKAN KAEDAH

CARIAN PINTAR BERASASKAN POPULASI

Oleh

ATHRAA ALI KADHEM

��2017

Pengerusi : Profesor Madya Noor Izzri Abdul Wahab, PhD Fakulti : Kejuruteraan

Aspek kebolehpercayaan sesuatu sistem penjanaan ialah perkara penting dalam

perancangan pengembangan kapasiti sistem tersebut untuk masa hadapan kerana ia

menentukan bahawa jumlah kapasiti yang dijana dapat menjamin bekalan elektrik

yang mencukupi, oleh yang demikian, aspek kebolehpercayaan sistem janakuasa ini

adalah penting untuk dikaji. Penilaian terhadap kebolehpercayaan bagi menentukan

bahawa bekalan yang dijana oleh sistem adalah mencukupi biasanya dikira

berdasarkan sama ada kaedah analisis atau kaedah simulasi. Kaedah simulasi Monte

Carlo atau Monte Carlo simulation (MCS) membolehkan penilaian indeks

kebolehpercayaan dilakukan secara tepat. Namun begitu, kaedah MCS ini mempunyai

kelemahan kerana ia tidak sesuai untuk sistem yang memiliki komponen yang banyak

atau berprestasi tinggi yang memerlukan tahap kebolehpercayaan tinggi lantaran

usaha lebih gigih diperlukan untuk membuat pengiraan dan hal ini akan mengambil

masa lebih lama bagi memastikan penumpuan yang lebih cekap. Kajian ini bertujuan

meneliti prestasi tiga jenis teknik yang dicadangkan untuk mengkaji penilaian

kebolehpercayaan sistem kuasa iaitu Disparity Evolution Genetic Algorithm (DEGA),

Binary Particle Swarm Optimisation (BPSO), dan Differential Evolution Optimization

Algorithm (DEOA). Ketiga-tiga algoritma pintar yang dicadangkan ini bergantung

pada teknik-teknik carian pintar berasaskan populasi atau population intelligent search

(PIS) yang dianggap menjimatkan bagi menggantikan kaedah MCS dalam menilai

indeks kebolehpercayaan sistem yang tidak mengikut kronologi. Kelebihan algoritma

jenis ini sangatlah jelas kerana ia dapat meningkatkan kelajuan pengiraan bagi

memastikan ketepatan yang lebih jitu dengan usaha yang paling minimum. Dalam

tahun-tahun kebelakangan ini, pembangunan sistem janakuasa menggunakan kuasa

angin bagi memenuhi permintaan terhadap bekalan elektrik telah mendapat perhatian

ramai. Sungguhpun demikian, sumber tenaga ini begitu berbeza daripada sumber-

sumber tenaga lain yang diperoleh secara konvensional kerana sifatnya yang terputus-

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putus secara semula jadi dan mungkin membawa kepada risiko tahap tinggi pada

kebolehpercayaan sistem elektrik. Oleh itu, ketiga-tiga teknik PIS baharu ini (yakni,

DEGA, DEOA, and BPSO) telah dicadangkan bagi menilai kebolehpercayaan sistem

janaan kuasa berintegrasikan tenaga angin. Kaedah-kaedah ini terbukti mampu

membuat anggaran nilai-nilai indeks kebolehpercayaan yang sangat tepat dengan

usaha yang paling minimum. Dalam kajian ini, analisis telah dilakukan terhadap ciri-

ciri data kelajuan angin dan penilaian keupayaan kuasa angin yang direkodkan di tiga

lokasi terpilih di Malaysia iaitu Mersing, Kudat, dan Kuala Terengganu. Keputusan

menunjukkan Mersing dan Kudat sesuai dijadikan lokasi untuk kajian kuasa angin.

Dapatan kajian ini telah menghasilkan bukti yang menyokong dapatan kajian-kajian

lepas yang dilakukan secara berasingan di Mersing dan Kudat. Hal ini menunjukkan

bahawa lokasi-lokasi ini berpotensi untuk dimanfaatkan bagi membina loji kuasa

angin yang baharu di Malaysia. Selain itu, kajian ini telah membina satu model

ramalan bagi kelajuan angin bagi tiga lokasi terpilih ini di Malaysia. Model ini telah

mengambil kira perubahan kelajuan angin bermusim bagi tahun berkenaan dalam

format model hibrid yang menggabungkan model Weibull dengan rangkaian neural

buatan supaya nilai-nilai ralat peramalan angin adalah lebih rendah daripada nilai-nilai

yang diramal menggunakan model Weibull semata-mata. Maka kuasa angin telah

dicadangkan agar disambungkan kepada Sistem Ujian Roy Billinton atau Roy

Billinton Test System (RBTS) daripada dua lokasi berkenaan di Malaysia.

Perbandingan telah dibuat terhadap nilai-nilai indeks kebolehpercayaan sebelum dan

selepas kedua-dua ladang diambil kira dalam sistem yang dikaji. Berdasarkan pada

analisis ini, dengan mengambil kira kuasa angin daripada kedua-dua lokasi berkenaan,

nilai-nilai indeks kebolehpercayaan telah menunjukkan sedikit peningkatan pada

kebolehpercayaan RBTS.

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ACKNOWLEDGEMENTS

In the Name of Allah, Most Gracious, Most Merciful, all praise and thanks are due to

Allah, and peace and blessings be upon His Messenger. I would like to express the

most sincere appreciation to those who made this work possible; supervisory

members, family and friends.

Firstly, I would like to express my great gratitude to my respected supervisor Assoc.

Prof. Ir. Dr. Noor Izzri Abdul Wahab for his invaluable advice and comments, constant

encouragement, guidance, support and patience all the way through my study work.

Equally the appreciation extends to the supervisory committee members Prof. Dr.

Ishak Bin Aris and Assoc. Prof. Dr. Jasronita BT Jasni and Assoc. Prof. Dr. Ahmed

N. Abdalla for providing me the opportunity to complete my studies under their

valuable guidance.

I would also like to acknowledge the Electrical Engineering Department of Universiti

Putra Malaysia for providing the numerous facilities and support for this research

work.

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This thesis was submitted to the Senate of the Universiti Putra Malaysia and has been

accepted as fulfilment of the requirement for the degree of Doctor of Philosophy. The

members of the Supervisory Committee were as follows:

Noor Izzri Bin Abdul Wahab, PhD, PEngAssociate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Ishak Bin Aris, PhD Professor



(Member)

Jasronita Bt Jasni, PhD Associate Professor



(Member)

Ahmed N. Abdalla, PhD Associate Professor

Faculty of Engineering Technology

University Malaysia Pahang

(Member)

ROBIAH BINTI YUNUS, PhD Professor and Dean

School of Graduate Studies


Date:

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Declaration by graduate student

I hereby confirm that:

� this thesis is my original work;

� quotations, illustrations and citations have been duly referenced;

� this thesis has not been submitted previously or concurrently for any other degree

at any institutions;

� intellectual property from the thesis and copyright of thesis are fully-owned by

Universiti Putra Malaysia, as according to the Universiti Putra Malaysia

(Research) Rules 2012;

� written permission must be obtained from supervisor and the office of Deputy

Vice-Chancellor (Research and innovation) before thesis is published (in the form

of written, printed or in electronic form) including books, journals, modules,

proceedings, popular writings, seminar papers, manuscripts, posters, reports,

lecture notes, learning modules or any other materials as stated in the Universiti

Putra Malaysia (Research) Rules 2012;

� there is no plagiarism or data falsification/fabrication in the thesis, and scholarly

integrity is upheld as according to the Universiti Putra Malaysia (Graduate

Studies) Rules 2003 (Revision 2012-2013) and the Universiti Putra Malaysia

(Research) Rules 2012. The thesis has undergone plagiarism detection software

Signature: _______________________ Date: __________________

Name and Matric No: Athraa Ali Kadhem, GS40780

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Declaration by Members of Supervisory Committee

This is to confirm that:

� the research conducted and the writing of this thesis was under our supervision;

� supervision responsibilities as stated in the Universiti Putra Malaysia (Graduate

Studies) Rules 2003 (Revision 2012-2013) were adhered to.

Signature:

Name of Chairman

of Supervisory

Committee:

Associate Professor

Dr. Noor Izzri Bin Abdul Wahab

Signature:

Name of Member

of Supervisory

Committee:

Professor

Dr. Ishak Bin Aris

Signature:

Name of Member

of Supervisory

Committee:

Associate Professor

Dr. Jasronita Bt Jasni

Signature:

Name of Member

of Supervisory

Committee:

Associate Professor

Dr. Ahmed N. Abdalla

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TABLE OF CONTENTS

Page

ABSTRACT i

ABSTRAK iii

ACKNOWLEDGEMENTS v

APPROVAL vi

DECLARATION viii

LIST OF TABLES xv

LIST OF FIGURES xx

LIST OF ABBREVIATIONS xxvi

CHAPTER

1 INTRODUCTION 1 1.1 Overview 1 1.2 Power System Reliability Assessment with Wind Energy 3 1.3 Problem Statement 5 1.4 Thesis Objectives 7 1.5 Scope of Research 7 1.6 Thesis Outline 7

2 LITERATURE REVIEW 9 2.1 Introduction 9 2.2 Generation Systems Adequacy Assessment 9

2.2.1 Generating Unit Model 10 2.2.2 Load Model 12 2.2.3 Risk Model for Generating System 13

2.3 Probabilistic Methods for Generation Systems Reliability

Evaluation 14 2.3.1 Analytical Methods 14 2.3.2 Monte Carlo Simulation 15

2.4 Populations-Based Intelligent Search (PIS) Methods 18 2.5 PIS for Reliability Assessment 18 2.6 Inclusion of Wind Energy in Generation Systems Adequacy 22 2.7 Overview of Stochastic Wind Speed /Wind Power Generation

model 24 2.7.1 Wind Speed Model 24 2.7.2 Wind Power Generation Model 25

2.8 Wind Power Potential at Specific Sites in Malaysia 26 2.9 Summary 27

3 RELIABILITY ASSESSMENT OF GENERATION SYSTEMS USING POPULATION-BASED INTELLIGENT SEARCH METHODS 28 3.1 Introduction 28

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3.1.1 Methodology of the Proposed PIS Techniques 28 3.2 Reliability Indices for Generation System 29 3.3 State Space 31 3.4 Genetic Algorithm 32

3.4.1 Basic Structure of Genetic Algorithm 32 3.4.2 Implementation Procedures of GA to Calculate Reliability

Indices 33 3.4.3 Stopping Criterion 34 3.4.4 Effect of GA Parameters 34

3.5 Generating Capacity Adequacy Assessment using the DEGA

Method 35 3.5.1 Basis of the Proposed DEGA Method 37 3.5.2 The Main Steps of DEGA for Reliability Assessment 39

3.6 Calculating Reliability Indices 43 3.7 Generating Capacity Adequacy Assessment using the DEOA

Method 44 3.7.1 Basis of DEOA Algorithm 44 3.7.2 DEOA Parameters 47 3.7.3 The Main Steps of DEOA for Reliability Assessment 47

3.8 Generating Capacity Adequacy Assessment using the BPSO

Method 49 3.8.1 Basis of BPSO Algorithm 50 3.8.2 BPSO Parameters 51 3.8.3 The Main Steps of BPSO for Reliability Assessment 52

3.9 Test Systems Description 54 3.10 Reliability Assessment of Generation Systems Adequacy Using

the Population-Based Intelligent Techniques 55 3.10.1 Performance Evaluation of DEGA Algorithm for the

IEEE-RTS-79 System 55 3.10.2 Performance Evaluation of DEGA Algorithm for the

IEEE-RTS-96 System 59 3.10.3 Performance Evaluation of DEOA Algorithm for the

IEEE-RTS-79 System 60 3.10.4 Performance Evaluation of DEOA Algorithm for the

IEEE-RTS-96 System 63 3.10.5 Performance Evaluation of BPSO Algorithm for the

IEEE-RTS-79 System 63 3.10.6 Performance Evaluation of BPSO Algorithm for the

IEEE-RTS-96 System 66 3.11 Comparison of Results Obtained From the Proposed Techniques 67 3.12 Summary 70

4 ADEQUACY ASSESSMENT MODEL OF THE GENERATING SYSTEM WITH INTEGRATION OF THE WIND POWER 72 4.1 Introduction 72

4.1.1 Overall Methodology 72 4.2 Reliability Assessment of Generating System with Wind Energy 73

4.2.1 Generating System Model 73

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4.3 Wind Energy Conversion System Models 77 4.3.1 Wind Speed Model of a Wind Turbine 77 4.3.2 Wind Turbine Power Model 80 4.3.3 Implementation Procedures for Wind Speed Weibull

Distribution Modeling 82 4.4 Impacts of Wind Turbine Unit Availability in Power Output from

WECS 85 4.5 Generating Systems Reliability Assessment Including Wind

Power Generation 86 4.5.1 Results and Analysis for IEEE-RTS-79 Incorporating with

Wind Power 88 4.5.2 Reliability Indices for IEEE-RTS-79 Incorporating with

Wind Power 91 4.6 Reliability Evaluation of the Generating Systems Containing

Wind Power Using the PIS Algorithms 92 4.7 Implementation Procedures of PIS Algorithms to Calculate

Reliability Indices of Generating Systems with Wind Power 93 4.7.1 Simulation of WTG Wind Speed/Power 94 4.7.2 Main Steps for Reliability Assessment Using PIS

Algorithms in the Presence of Wind Power 96 4.8 Results and Analysis 98

4.8.1 Case Study 98 4.8.2 Reliability Indices for IEEE-RTS-79 Using PIS Including

Wind Power Generation 100 4.9 Summary 102

5 ESTIMATION OF WIND ENERGY POTENTIAL FOR POWER SYSTEM RELIABILITY ASSESSMENT IN MALAYSIA 103 5.1 Introduction 103 5.2 Description of the Wind Speed Data Collected in Malaysia 103 5.3 Methodology of Statistical Analysis for Wind Speed Data

Wind Speed Carrying Maximum Energy 10� 5.4 A Statistical Analysis Model for Wind Speed Data for Wind

Energy Potential at the Various Wind Sites 108 5.4.1 Mean Wind Speed and Standard Deviation 108 5.4.2 Wind Speed in Frequency Distribution Format 109 5.4.3 Wind Speed Direction Characteristics 109

5.5 Weibull Distribution Model for Analysis Wind Potential 109 5.6 Methods for Estimating the Weibull Parameters 110

5.6.1 Moment Method (MM) 110 5.6.2 The Least Square Method (LSM) 111 5.6.3 Modified Maximum Likelihood Method (MMLM) 111 5.6.4 Empirical Method (EM) 112 5.6.5 Performance of the Weibull Parameters Estimation

Methods 112 5.7 Estimation of the Variation of Wind Speed and Wind Power with

Height 113 5.7.1 Extrapolation of Wind Speed at Different Heights 113

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5.7.2 Evaluation of Most Probable Wind Speed & Wind Speed

Carrying Maximum Energy 114 5.7.3 Estimation of Wind Power Density and Wind Energy

Density 114 5.7.4 Estimation of the Wind Turbine Output Power and

Capacity Factor 115 5.8 Model for the Wind Speed Prediction 116

5.8.1 Performance of the Weibull Time Series Model for

Wind Speed 117 5.8.2 Description of the Artificial Neural Network Model 117 5.8.3 Analysis of the Prediction Error 119

5.9 The Potential of Wind Energy at Specific Sites in Malaysia 120 5.9.1 Reliability Analysis of Generation System Including

Wind Energy Using Hourly Mean Wind Speed at

Specific Sites in Malaysia 120 5.9.2 Proposed Reliability Assessment of the Generating

System with Wind Energy 121 5.10 Results and Analysis of the Wind Speed Assessment at Specific

Sites in Malaysia 123 5.10.1 Calculation of Mean Wind Speed 123 5.10.2 Frequency Distribution of Wind Speed Data 128 5.10.3 Wind Rose Representation Based on Hourly Wind Speed 129 5.10.4 Weibull Distribution Representation Based on Hourly

Wind Speed Data 131 5.10.5 Calculating Weibull Parameters Based on the Hourly

Wind Speed Data 133 5.11 Calculation of the Wind Speed and Wind Power Density with

Different Height 140 5.11.1 Calculate of Average Wind Speed with Different Height 140 5.11.2 Calculation of Wind Power Density and Energy at

Different Heights 145 5.11.3 Selecting of the Suitable Wind Turbine Units at Malaysia

Sites 149 5.12 Implementation of the Combined Weibull and ANN Method for

Prediction and Simulation of Wind Speed 152 5.13 Reliability Assessment of Generating System Containing Wind

Power from Selection Sites at Malaysia 156 5.13.1 Reliability Test Systems Description 156 5.13.2 Case Studies 157 5.13.3 Reliability Indices for RBTS Including Wind Power

Generation 160 5.14 Summary 161

6 CONCLUSIONS AND SUGGESTIONS FOR FUTURE WORKS 163 6.1 Conclusion 163 6.2 Contributions of this Research 164 6.3 Suggestions for Future Work 165

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REFERENCES 166 APPENDICES 183

BIODATA OF STUDENT 203

LIST OF PUBLICATIONS 204

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LIST OF TABLES

Table Page

2.1 Reliability Standard LOLE in 10 countries 13

2.2 Reliability indices calculate based on non-sequential & sequential

MCS with LHS techniques 17

2.3 Reliability indices calculate based on non-sequential MCS and

sequential MCS with CE techniques 17

2.4 Reliability indices calculated based on PIS techniques 20

3.1 The comparison between the proposed algorithms based on the

operating parameters 54

3.2 The IEEE-RTS-79 and IEEE-RTS-96 systems 55

3.3 DEGA results for IEEE-RTS-79 56

3.4 Results of 100 repeated runs of DEGA 57

3.5 Comparison of computational time among unit addition, MSGA,

and MCS methods 57

3.6 State array from the DEGA for IEEE-RTS-79 58

3.7 Comparison of LOLE hours per year for different load curves using

DEGA algorithm and previous methods 59

3.8 Comparison of LOEE per year for different load curves using

DEGA algorithm and previous methods 59

3.9 Comparison of LOLF annual occurrences for different load curves

using DEGA algorithm and previous methods 59

3.10 DEGA results for IEEE-RTS-96 60

3.11 DEOA results for IEEE-RTS-79 61

3.12 Results of 100 repeated runs of DEOA algorithm 61

3.13 Comparison of computational time among MCS, MSGA, and

DEOA algorithms 62

3.14 DEOA results for the IEEE-RTS-96 63

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3.15 BPSO results for IEEE-RTS-79 64

3.16 Results of 250 repeated runs of BPSO algorithm 64

3.17 Comparison of computational time among MCS, MSGA, and

BPSO algorithms 66

3.18 State array which is obtained from the BPSO algorithm for the

IEEE-RTS-79 system 66

3.19 BPSO results for the IEEE-RTS-96 67

3.20 Comparison of reliability indices among analysis method, DEGA,

DEOA, BPSO for IEEE-RTS-79 system 68

3.21 Comparison of reliability indices among analysis method, MCS,

EPSO, DEGA, DEOA, and BPSO for IEEE-RTS-96 system 69

4.1 Number of identical generating units, rated capacity, and annual

failure rate during year for the RTS-79 74

4.2 Comparison among SMCS and SMCS with F & D for reliability

indices evaluation vs a number of samples 88

4.3 Reliability indices of the IEEE-RTS-79 92

4.4 Proposed PIS algorithms control parameters 99

4.5 Comparison between SMCS and the proposed PIS methods 100

4.6 Comparison between proposed PIS methods and different methods

in literature 101

5.1 Description of the wind speed measuring stations at selected regions

in Malaysia 105

5.2 Annual monthly mean wind speed and standard deviation at

Mersing from 2013 to 2015 126

5.3 Annual monthly mean wind speed and standard deviation at Kudat

from 2013 to 2015 127

5.4 Annual monthly mean wind speed and standard deviation at Kuala

Terengganu from 2013 to 2015 127

5.5 Measured wind speed arranged in frequency and cumulative

distribution format of equal width of 0.5 m/s at Mersing location 128

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5.6 Values of Weibull parameters estimation by four numerical

methods and statistical test (RMSE) for Mersing site based on

hourly wind speed data for (2015) 135


methods and statistical test (RMSE) for Kudat site based on hourly

wind speed data for (2015) 136


methods and statistical test (RMSE) for Kuala Terengganu site

based on hourly wind speed data for (2015) 137

5.9 Yearly values of Weibull parameters estimated by four numerical

methods and statistical test (RMSE) for Mersing, Kudat, and Kuala

Terengganu based on daily wind speed from data collected from

(2013-2015) 138

5.10 Yearly values of Weibull parameters estimating by four numerical

methods and statistical test (RMSE) for Mersing, Kudat, and Kuala

Terengganu based on hourly wind speed from data collected from

(2013-2015) 139

5.11 Monthly and annual mean wind speed (m/s) in Mersing at a

different heights above ground level 143

5.12 Monthly and annual mean wind speed (m/s) in Kudat at a different

heights above ground level 143

5.13 Monthly and annual mean wind speed (m/s) in Kuala Terengganu

at a different heights above ground level 144

5.14 Wind power and energy density characteristics at height of 43.6 m

in Mersing 145

5.15 Wind power and energy density characteristics at height of 100 m

in Mersing 146

5.16 Wind power and energy density characteristics at height of 3.5 m in

Kudat 146


in Kudat 147

5.18 Wind power and energy density characteristics at height of 5.2 m in

Kuala Terengganu 147


in Kuala Terengganu 148

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5.20 The technical data of wind turbines 149

5.21 Reliability indices at various sites 161

A.1 Weekly peak load as a percentage of annual peak 183

A.2 Daily peak load as a percentage of weekly peak 183

A.3 Hourly peak load as a percentage of daily peak 184

C.1 Measured wind speed data arranged in frequency and cumulative

distribution format of equal width of 0.5 m/s at Kudat location 191

C.2 Measured wind speed data arranged in frequency and cumulative

distribution format of equal width of 0.5 m/s at Kuala Terengganu

location 192

D.1 Values of Weibull parameters estimating by four numerical

methods and statistical test (RMSE) for Mersing, based on hourly



methods and statistical test (RMSE) for Mersing, based on hourly



methods and statistical test (RMSE) for Kudat, based on hourly



methods and statistical test (RMSE) for Kudat, based on hourly



methods and statistical test (RMSE) for Kuala Terengganu based on



methods and statistical test (RMSE) for Kuala Terengganu based on


E.1 Wind power and energy density characteristics at height of 60 m at

Mersing 199


Mersing 199

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Kudat 200


Kudat 200





F.1 The RBTS generating unit ratings and reliability data 202

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LIST OF FIGURES

Figure Page

1.1 Structures of hierarchical levels in a power system 2

1.2 Techniques used for power systems reliability analysis 3

1.3 Global Cumulative Installed Wind Capacities 2000-2015 4

2.1 The risk model in the generating capacity reliability evaluation 10

2.2 Markovian models for conventional units 11

2.3 Relationship between MTTR, MTTF and MTBF 11

2.4 Chronological hourly load model for the IEEE-RTS 13

2.5 Generating capacity adequacy assessment modeling for systems

containing wind energy 23

3.1 Overall flow chart for obtaining reliability indices of generation

system 29

3.2 Analysis of system states in the whole state space 31

3.3 The flow of Genetic Algorithm 33

3.4 Flowchart of the DEGA 36

3.5 The mutation operation of the disparity model 37

3.6 The chromosome representation 37

3.7 The chromosome structure within the state array 38

3.8 Flowchart of the calculating of reliability indices based on the

DEGA technique 42

3.9 Main steps of DEOA 45

3.10 Flowchart of the calculation of reliability indices based on the

DEOA technique 49

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3.11 RTS-79 Evolution of estimated LOLE (y-axis) with the number of

100 repeated runs (x-axis): analysis results (dashed curve

represent the real value) versus DEOA results (line converging to

exact result) 61

3.12 RTS-79 Evolution of estimated LOEE (y-axis) with the number of


represent the real value) versus DEOA results (line converging to

exact result) 62

3.13 RTS-79 Evolution of estimated LOLE (y-axis) with the number of


represent the real value) versus BPSO results (line converging to

exact result) 65

3.14 RTS-79 Evolution of estimated LOEE (y-axis) with the number of


represent the real value) versus BPSO results (line converging to

exact result) 65

3.15 Performance comparison of the proposed algorithms with analysis

method for the IEEE-RTS-79 system for LOLE reliability index 68


method for the IEEE-RTS-79 system for LOEE reliability index 68


and MCS methods for the IEEE-RTS-96 system for LOLE

reliability index 69


and MCS methods for the IEEE-RTS-96 system for LOEE

reliability index 70

4.1 Risk model of the generating system adequacy containing wind

energy 73

4.2 Capacity output model for a 12-MW unit from the IEEE-RTS-79 75

4.3 IEEE-RTS-79 total generation output (1-year sample) 75

4.4 Superimposition of system capacity and load 76

4.5 Intersections of outage capacity within the whole year in this

simulation 76

4.6 Weibull distribution density versus wind speed under a constant

value of c=7 and different values of k 78

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4.7 Weibull distribution density versus wind speed under a constant

value of k=2 and different values of c 78

4.8 Simulated wind speed sample for (360) hours 80

4.9 A typical power curve of the WTG 81

4.10 Flowchart for the WTG power output model 83

4.11 Power output from a single WTG in a sample one month 84

4.12 Power output from a wind farm in a sample one month 84

4.13 Simulation WTG output power with associated failure for

sampling year 89

4.14 Simulation WECS output power with associated failure for

sampling year 89

4.15 Available system capacity obtained from generating unit and the

WECS 90

4.16 The available capacity of the system which, superimposition with

chronological available load model 90

4.17 Simulated outages of output power with associated failure 91

4.18 A 300 hour mean wind speed simulation for Swift current site 94

4.19 Simulated wind speed for the Swift current site for a sample year 95

4.20 Power output from Swift current wind farm in a sample year 95

4.21 Frequency and duration of capacity outages probability with

associated failure 99

4.22 Performance comparisons between the proposed algorithms and

MCS method for the IEEE-RTS-79 system for LOLE reliability

index 101

4.23 Performance comparisons between the proposed algorithms and

MCS method for the IEEE-RTS-79 system for LOEE reliability

index 102

5.1 Malaysia location in the South East part 104

5.2 Wind speed distribution maps for stations sites using in this study

in Malaysia 105

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5.3 Flow chart to explain the steps for wind speed data analysis form

Sections 5.4 to 5.10 107

5.4 An ANN architecture for the hourly wind speed prediction 118

5.5 Flow chart showing reliability assessment for generation system

including wind generating sources 122

5.6 The monthly wind speed data in Mersing over 3 years period 123

5.7 The monthly wind speed data in Kudat over 3 years period 124

5.8 The monthly wind speed data in Kuala Terengganu over 3 years

period 124

5.9 Hourly mean wind speed variation at Mersing throughout the year 125

5.10 Hourly mean wind speed variation at Kudat throughout the year 125

5.11 Hourly mean wind speed variation at Kuala Terengganu

throughout the year 125

5.12 Wind rose diagrams for wind speed measured station in Malaysia

from 2013 to 2015 at the location Mersing 129


from 2013 to 2015 at the location Kudat 130


from 2013 to 2015 at the location Kuala Terengganu 130

5.15 Shown the wind class frequency distribution at locations (a),

Mersing (b), Kudat (c) and Kuala Terengganu 131

5.16 Comparison between Weibull distribution function and others

distributions functions for recorded wind data for the whole year

for Mersing (2015) 132



for Kudat (2015) 132



for Kuala Terengganu (2015) 133

5.19 Monthly and annual mean wind speeds in the Mersing 141

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5.20 Monthly and annual mean wind speeds in the Kudat 141

5.21 Monthly and annual mean wind speeds in the Kuala Terengganu 142

5.22 Comparison of the capacity factors obtained for different wind

turbines at various heights for Mersing 150


turbines at various heights for Kudat 150


turbines at various heights for Kuala Terengganu 151

5.25 Annual energy output generated by all wind turbines for all

regions at 100 m 151

5.26 Comparison between measured wind speed data and simulated

wind speed data from Weibull model 153

5.27 Comparison between measured and predicted wind speed for two

weeks from Mersing/2015 in Malaysia, for validation of the

combined method 154

5.28 Comparison between measured and predicted wind speed for two

months from Mersing/2015 in Malaysia, for validation of the

combined method 154

5.29 Comparison between measured and predicted hourly wind speed

data for six months for Mersing, for validation of the combined

method 155


data for six months for Kudat, for validation of the combined

method 155


data for six months for Kuala Terengganu, for validation of the

combined method 156

5.32 Single line diagrams of the RBTS 157

5.33 Simulation of the output power from WTG for sampling year 158

5.34 Simulation of the output power from wind farm for sampling year 159

5.35 The available capacity of the generation system which,

superimposition with chronological available load model 159

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5.36 Simulation of reliability indices with (600) sampling years 160

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LIST OF ABBREVIATIONS

ACS Ant Colony System

AEP Annual Energy Production

ANN Artificial Neural Network

BACS Binary Ant Colony System

BPSO Binary Particle Swarm Optimization

CE Cross-Entropy

DE Differential Evolution

DEGA Disparity Evolution Genetic Algorithm

DEO Differential Evolution Optimization

DEOA Differential Evolution Optimisation Algorithm

DPLVC Daily Peak Load Variation curve

EA Evolutionary Algorithm

EC Evolutionary Computation

EM Empirical Method

EPSO Evolutionary Particle Swarm Optimization

F & D Frequency and Duration

FOR Forced Outage Rate

GA Genetic Algorithm

GOF Goodness Of Fit

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HL Hierarchical levels

HLΙ Hierarchical level-I

HLΙΙ Hierarchical level-II

HLΙΙΙ Hierarchical level-III

Ia lagging strand

Ie leading strand

ISSP Intelligent State Space Pruning

LDC Load Duration Curve

LHS Latin Hypercube Sampling

LOEE Loss Of Energy Expectation

LOLD Loss Of Load Duration

LOLE Loss Of Load Expectation

LOLF Loss Of Load Frequency

LOLP Loss Of Load Probability

LSM Least Square Method

MCS Monte Carlo Simulation

MM Moment Method

MMD Malaysia Meteorological Department

MMLM Modified Maximim Likelihood Method

MSGA Modified Simple Genetic Algorithm

MTBF Mean Time Between Failure

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MTTR Mean Time To Repair

Pc crossover probability

PDF Probability Density Function

PIS population-based intelligent search

Pm Mutation Probability

POCT Probability Outage Capacity Table

pop_size Population size

PSO Particle Swarm Optimization

RBTS Roy Billinton Test System

RMSE Root Mean Square Error

SMCS Sequential Monte Carlo Simulation

VRT Variance Reduction Techniques

WECS Wind Energy Conversion System

WTG Wind Turbine Generator

λ Failure Rate

μ Repair Rate

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CHAPTER 1

1 INTRODUCTION

1.1 Overview

The main purpose of the electrical power system is the efficient and cost-effective

generation of electrical power for its customers, with good consideration of its quality

and supply continuity. The contemporary power quest requires the utility companies

to meet up with the consumers’ electrical energy demand (Billinton & Allan, 1996)

which, otherwise, may lead to a huge loss of income to the generators as well as the

consumers. However, it is neither economically nor technically feasible to build an

absolutely reliable power system due to some uncontrollable factors, such as the

random failures of components, time-dependent energy resources, and load

fluctuation.

Quantitative reliability assessments should not only evaluate a system’s actual physical components in terms of performance and random behavior, but also the

overall requirements, procedures, and engineering issues inherent in the system’s operation (Wafa et al., 2006). A power system is an extremely complex, advanced and

integrated structure (Cepin, 2011); even the most advanced computer programs lack

the capacity for the comprehensive and holistic interpretation of the system.

Consequently, power systems are frequently divided into appropriate subsystems that

can be separately analyzed.

The most convenient means for segmenting an electrical power system are its main

functional zones; namely its generating capacity systems, composite systems, and

distributed power systems. Thus, Hierarchical Levels (HL) has been developed

(Billinton & Allan, 1984) to determine an identical means of grouping and identifying

the aforementioned functional zones, as illustrated in Figure 1.1. From the figure,

Hierarchical Level Ι (HLI) refers to the facilities responsible for generating adequate power to meet the system demand whereas Hierarchical Level ΙΙ (HLII) refers the composite generation and transportation network that is capable of transporting the

generated energy to the major consumption or sub-transmission points. Hierarchical

Level ΙΙΙ (HLIII) refers to the entire system as well as the distribution system that is

capable of disbursing individual customers’ energy demand. This research work focuses on the analysis of the system adequacy that is conducted at the HLI.

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Figure 1.1 : Structures of hierarchical levels in a power system

The term reliability index, when applied to generation systems, refers to performance

measures of the generating system capacity that can influence the continuity of

electrical power supply to the customer. Two basic concepts are used in system

capacity assessments which are adequacy and security. Generating capacity

requirements can also be separated into two basic categories i.e., static capacity, which

correlates with the long-term estimate of overall system demand; and operating

capacity, which is a short-term correlation with the actual capacity required to meet a

specified load. Adequacy assessment considers the entirety of the facilities within a

system and their sufficiency to satisfy consumer load demands. Adequacy is therefore

associated with a static level of demand which is exclusive of transient system

disturbances (Billinton & Li, 1994). System security is defined as the ability of the

electric system to withstand sudden disturbances such as electric short circuits or

unanticipated loss of system elements. Security concept is therefore associated with

operating capacity (Billinton & Allan, 1996).

Reliability analysis of the power system can be conducted by using various techniques

as shown in Figure 1.2. Historically, power system reliability analyses have been

carried out utilizing deterministic methods. These techniques use simple rule of thumb

methods and are easy to apply. A major drawback of the deterministic approach is that

it cannot respond adequately to the random behavior of the power system and

customer demands. Meanwhile, probabilistic reliability evaluation methods can

recognize random system characteristics, and are more suitable in modern power

system applications. These assessment methods comprise of analytical and simulation

techniques and both approaches are used in electric power utilities at the present time.

Simulation techniques are used to imitate unpredictable performance in power

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systems, either in a random or sequential way (Pereira & Balu, 1992). Whereas,

analytical assessment methods are easily and simply applied using mathematical

analysis to derive precise analytical solutions to the value of reliability indices from

the model (Billinton & Allan, 1988). Monte Carlo simulation is the preferred method

for reliability assessment of large and complex systems due to the realism it

introduces; therefore, it is adopted as the benchmark when comparing accuracies

among different computational methods.

Figure 1.2 : Techniques used for power systems reliability analysis

In recent years, many efforts have been made in order to improve the computational

efficiency of algorithms, especially those that are applied to problems of great

complexity in power systems. Many optimization algorithms have been developed

utilizing the nature of population-based intelligent search (PIS); these algorithms use

all possible solutions by iteratively and stochastically changing rather than focusing

on improving a single solution. Examples of these algorithms include the ant colony

system (ACS), genetic algorithm (GA), particle swarm optimization (PSO), intelligent

state space pruning (ISSP), and evolutionary computation (EC) (Benidris et al., 2015;

Benidris & Mitra, 2014). These techniques have been utilized in different forms by

researchers in the adequacy assessment of power system.

1.2 Power System Reliability Assessment with Wind Energy

Wind power has remarkable economic and environmental advantages when compared

to other power generation sources (Sina et al., 2014). Presently, wind power is

considered to be an essential alternative source for generating power. In addition,

growing demand for electrical energy and the concerns associated with limited

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reserves of fossil fuels such as oil, natural gas, and coal are also responsible for the

development and increase in wind energy utilization.

Wind power is the most promising renewable energy and is one of the fastest

developing electric generating technologies in the whole world (Shi & Lo, 2013).

Consequently, the pervasiveness of the wind power in power systems is increased.

Figure 1.3 shows the global installed wind power capacity around the world from 2000

to 2015. A total of 432.419 MW of capacity had been installed world by the end of

2015 (GWEC, 2016).

The growing pervasiveness of wind energy in power system has a huge influence on

the electrical system’s reliability in relation to other conventional sources for power generation. Indeed, intermittent nature of wind generation gives rise to several issues;

these issues may include the operation, planning of power systems with wind energy

sources (Zheng & Zhong, 2010).

Figure 1.3 : Global Cumulative Installed Wind Capacities 2000-2015 (GWEC,

2016)

One of the great challenges of integrating wind energy into power systems can be seen

from the reliability assessment perspective. Particularly, the method of involving wind

generation capacity into the overall generation capacity assessment is a major

challenge that often raises research questions.

Wind power has an interesting resource potential as well as technology and experience

when considering energy mix options. Analysis of wind data characteristics and

accurate wind power potential assessment at a given location is an imperative

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requirement before making a decision for the installation of WECS and evaluating

plans for relating these projects with electrical power grid or for a remote area.

It is most likely that electricity demand in Malaysia is going to rise in the future. From

the review of the several literatures, it is found that renewable resources seem to have

a sufficient potential to develop a sustainable electricity system (Ahmad & Tahar,

2014). It is a fact that, in some of the rural areas and in the coastal regions in Malaysia,

the electrical power can also be generated from wind energy. Even though the number

of studies had been done by researchers on the potential of wind energy for adopting

in Malaysia, and also there are a number of energy projects that are trying to adopt

wind turbines in both small and large scales (Goh. et al., 2016; Islam et al., 2011). So

far, these studies are not completed and still needed for a more investigation.

1.3 Problem Statement

Generation system reliability is an important aspect in the planning for future system

capacity expansion to make sure that the total installed capacity is sufficient to provide

adequate electricity when needed (Almutairi et al., 2015; Lin et al., 2014).

In general, an analytical approach is efficient to assess the reliability if not taking into

consideration the complex operating conditions and the random failure probability for

system components. Therefore, the analytical assessment methods have two main

drawbacks when evaluating power generation systems; firstly, relating to the system’s complexity and secondly, the number of potential system states, both of which

increase exponentially with the number of system components (Billinton & Allan,

1988). The Monte Carlo simulation (MCS) method enables accurate evaluation of

reliability indices. The main drawback of MCS is that it is requires large computational

effort, so that not suitable for a system with high-reliability, which would take a long

time to converge efficiently (Green II et al., 2011). It is important to note that MCS

need a large number of simulation years in order to obtain an accurate result. Because

a MCS is similar to a survey for calculating reliability indices, a result based on a

sample differs from the result that would have been based on the overall simulation.

Therefore, the margin of error associated to an estimator of a quantity of mean of

reliability indices must computed in order to know if results are reliable (Henneaux et

al., 2016). Also, MCS face some diffculties in dealing with rare events, e.g., to assess

very small values of reliability indices.

Both the MCS and PIS methods have the state space models but are different in their

mechanisms for sampling the reliability indices estimation in generation systems. So

far, evaluation of sampling state for the power systems is still a very computational

intensive issue and therefore needs much attention. A significant number of research

papers in the power system reliability assessment in literature have introduced

techniques using PIS methods, which were used to reduce the search space and the

computational efforts.

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Genetic algorithm (GA) is one of the powerful and primary methods that can be

applied as a stochastic search tool that is based on the PIS techniques (Zhao & Singh,

2010). Although often applied with successes, the performance of these algorithms

may suffer when applied on complex systems (Wang & Singh, 2007). Despite the

efforts done in studying the reliability evaluation of power systems, new algorithms

are needed as the complexity of the current power grid is being extended, by the

integration of new capacity sources with conventional sources in generating systems.

Furthermore, as the system complexity increases (in this context it means more WTGs

are integrated), the computational efficiency advantage of the population-based

stochastic search has become more evident.

The electric power systems continue to witness the high level of wind power

integration as a global phenomenon (Benidris & Mitra, 2014) raising concerns

associated with an effective power system planning and operation. The high level of

wind power penetration into the electrical system mandates the assessment of their

capacities and impacts on reliability by the system planning in line with their power

utilization and environmental benefits. So, high penetration of intermittent wind

energy resources into electric power systems require the need to investigate the system

reliability when adding large amounts of highly variable capacity that differ

considerably from conventional generation sources (Padma et al., 2014).

The production of wind energy often involves uncertainties as a result of the wind

speed stochastic nature and the variation on the power curve. A technique for

modelling wind speed plant in power system reliability evaluation requires the need

to investigate and give an effective model for estimating wind power. The uncertainty

of the wind power output therefore means the uncertainty of the power system

reliability level (Sina et al., 2014; Soleymani et al., 2015).

For an effective development of wind power, the electric utility should ensure

adequate survey of wind availability as the first measure. Regrettably, reliable wind

speed data proper for wind resource estimation are difficult to obtain (Anurag & Saini,

2014; Kidmo et al., 2015). Therefore, particular wind speed models are developed

from the available wind speed data records that have been previously collected. A

crucial requirement to evaluate wind-connected power system’s reliability is to precisely simulate its hourly wind speed. However, wind speed is time and site variant

and the speed at a particular hour is related to that which precedes it.

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1.4 Thesis Objectives

The specific objectives of this research are summarized as follows:

1. To propose new techniques based on PIS and compare with previously used

techniques and MCS for calculating reliability indices for the generation systems

adequacy reliability assessments in order to improve its computational time and

accuracy.

2. To apply the proposed techniques and compare with the performances of previous

methods in calculating the reliability indices to evaluate the power system

reliability assessment with the integration of the wind energy.

3. To propose a combined Weibull and ANN method to estimate wind speed data in

order to assess wind energy potential at any sites.

4. To suggest an effective statistical model for analysing wind speed data, in order to

assess wind power potential for specific sites in Malaysia. Additionally, the

capacity contribution of the wind power in the generating systems adequacy and

the impact on generation systems reliability are to be analysed.

1.5 Scope of Research

The scope of this research includes the development of HL-I adequacy evaluation

algorithms and methods for reliability assessment of generating systems containing

wind energy. The three-year (2013-2015) wind speed data used in the research works

were purchased from the Malaysia Meteorology Department consisting of wind data

from three sites, i.e. (Mersing, Kudat, and Kuala Terengganu). The obtained data is

used to assess wind power potential for specific sites in Malaysia. The system

adequacy analysis associated with static capacity levels to make sure that the total

installed capacity is sufficient to provide adequate electricity when needed. In order

to show the effectiveness and validation of the proposed algorithms, the IEEE

Reliability Test System (IEEE-RTS) are considered. All simulation works and coding

are done using several softwares such as Matlab, R-Studio package, and WRPLOT

View.

1.6 Thesis Outline

The rest of this thesis is organized as follows:

Chapter 2 presents background information pertaining to power system reliability and

its relevant aspects. This chapter also reviews related concepts and the available

techniques of generating system adequacy assessment and surveys the previously

developed models with regard to wind energy in particular.

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In Chapter 3, the required algorithms to evaluate the adequacy for conventional

generation are presented using the common PIS algorithms. In this chapter, new

techniques are proposed to calculate generation system adequacy indices. The

techniques are the Disparity Evolution Genetic Algorithm (DEGA), Differential

Evolution Optimization Algorithm (DEOA) and discrete Binary Particle Swarm

Optimization (BPSO). The results obtained are compared between these new

techniques and also with previously used techniques including the conventional MCS.

Chapter 4 presents the mathematical models necessary for performing the adequacy

evaluation of the electric power generating systems containing wind energy by means

of the sequential Monte Carlo simulation method and PIS techniques. The models of

the wind speed and that of the WTG are presented and described.

In Chapter 5, an effective statistical model for analyzing wind speed data, in order to

assess wind energy potential at specific locations in Malaysia. Four numerical

methods are employed to estimate the values of the shape parameter k, and the scale

parameter c. Furthermore, selection of wind turbine class and prediction of annual

energy production (AEP) from the proposed turbine are done. In addition, the study

developed a prediction hybrid model for the wind speed in order to simulate the

operation of power system with penetration of wind energy to assess the reliability of

the power systems.

Chapter 6 concludes the research works done and highlighting the contributions of this

thesis and discusses possible future works.

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