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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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unless otherwise stated. Use may be made of any material contained within the thesis
for non-commercial purposes from the copyright holder. Commercial use of material
may only be made with the express, prior, written permission of Universiti Putra
Malaysia.
Copyright © Universiti Putra Malaysia
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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
Faculty of Engineering
Universiti Putra Malaysia
(Member)
Jasronita Bt Jasni, PhD Associate Professor
Faculty of Engineering
Universiti Putra Malaysia
(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
Universiti Putra Malaysia
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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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
5.7 Values of Weibull parameters estimation by four numerical
methods and statistical test (RMSE) for Kudat site based on hourly
wind speed data for (2015) 136
5.8 Values of Weibull parameters estimation by four numerical
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
5.17 Wind power and energy density characteristics at height of 100 m
in Kudat 147
5.18 Wind power and energy density characteristics at height of 5.2 m in
Kuala Terengganu 147
5.19 Wind power and energy density characteristics at height of 100 m
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
wind speed data for (2013) 193
D.2 Values of Weibull parameters estimating by four numerical
methods and statistical test (RMSE) for Mersing, based on hourly
wind speed data for (2014) 194
D.3 Values of Weibull parameters estimating by four numerical
methods and statistical test (RMSE) for Kudat, based on hourly
wind speed data for (2013) 195
D.4 Values of Weibull parameters estimating by four numerical
methods and statistical test (RMSE) for Kudat, based on hourly
wind speed data for (2014) 196
D.5 Values of Weibull parameters estimating by four numerical
methods and statistical test (RMSE) for Kuala Terengganu based on
hourly wind speed data for (2013) 197
D.6 Values of Weibull parameters estimating by four numerical
methods and statistical test (RMSE) for Kuala Terengganu based on
hourly wind speed data for (2014) 198
E.1 Wind power and energy density characteristics at height of 60 m at
Mersing 199
E.2 Wind power and energy density characteristics at height of 65 m at
Mersing 199
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E.3 Wind power and energy density characteristics at height of 10 m at
Kudat 200
E.4 Wind power and energy density characteristics at height of 30 m at
Kudat 200
E.5 Wind power and energy density characteristics at height of 10 m at
Kuala Terengganu 201
E.6 Wind power and energy density characteristics at height of 30 m at
Kuala Terengganu 201
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
100 repeated runs (x-axis): analysis results (dashed curve
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
250 repeated runs (x-axis): analysis results (dashed curve
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
250 repeated runs (x-axis): analysis results (dashed curve
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
3.16 Performance comparison of the proposed algorithms with analysis
method for the IEEE-RTS-79 system for LOEE reliability index 68
3.17 Performance comparison of the proposed algorithms with analysis
and MCS methods for the IEEE-RTS-96 system for LOLE
reliability index 69
3.18 Performance comparison of the proposed algorithms with analysis
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
5.13 Wind rose diagrams for wind speed measured station in Malaysia
from 2013 to 2015 at the location Kudat 130
5.14 Wind rose diagrams for wind speed measured station in Malaysia
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
5.17 Comparison between Weibull distribution function and others
distributions functions for recorded wind data for the whole year
for Kudat (2015) 132
5.18 Comparison between Weibull distribution function and others
distributions functions for recorded wind data for the whole year
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
5.23 Comparison of the capacity factors obtained for different wind
turbines at various heights for Kudat 150
5.24 Comparison of the capacity factors obtained for different wind
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
5.30 Comparison between measured and predicted hourly wind speed
data for six months for Kudat, for validation of the combined
method 155
5.31 Comparison between measured and predicted hourly wind speed
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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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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