Islanding detection and classification and load shedding scheme for dispersed generation integrated radial distribution systems.

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ISLANDING DETECTION AND CLASSIFICATION AND LOAD SHEDDING SCHEME FOR DISPERSED GENERATION INTEGRATED

RADIAL DISTRIBUTION SYSTEMS

AZIAH KHAMIS

THESIS SUBMITTED IN FULFILMENT OF THE DEGREE OF DOCTOR OF PHILOSOPHY

FACULTY OF ENGINEERING AND BUILT ENVIRONMENT UNIVERSITI KEBANGSAAN MALAYSIA

BANGI

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PENGESANAN DAN PENGKELASAN KEPULAUAN DAN SKIM PENYISIHAN BEBAN BAGI SISTEM PENGAGIHAN

JEJARI TERSEPADU PENJANA TERAGIH

AZIAH KHAMIS

TESIS YANG DIKEMUKAKAN UNTUK MEMPEROLEH IJAZAH DOKTOR FALSAFAH

FAKULTI KEJURUTERAAN DAN ALAM BINA UNIVERSITI KEBANGSAAN MALAYSIA

BANGI

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DECLARATION

I hereby declare that the work in this thesis is my own except for quotations and summaries which have been duly acknowledged.

3 Nov 2014 AZIAH KHAMIS

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ACKNOWLEDGMENTS

First and foremost praise be to Almighty Allah for all His blessings for giving me perseverance and good health throughout the duration of this PhD research.

I would like to express sincere appreciation for the intelligent advice, encouragement and guidance of my main supervisor, Assoc. Prof. Dr. Hussain Shareef. Without his tireless assistance, leadership, and confidence in my abilities, this thesis would not come to its timely completion.

Furthermore, I would like to express my high appreciation to my co-supervisor Prof. Dr. Hjh. Azah Mohamed for the valued knowledge, ideas, encouragement, assistance and support received from her during my PhD program.

I would like to acknowledge the financial support from Ministry of Higher Education, Universiti Teknikal Malaysia Melaka and also Universiti Kebangsaan Malaysia for making it possible for me to pursue and complete my PhD degree.

I would like to thank all Power System Research group UKM for their help, friendship, and creating a pleasant working environment throughout my years in UKM. My heartfelt appreciation goes to all my special friends, especially Hazilah Abdullah and Abdul Zaini Abdullah for their inseparable support and prayers.

To my dearest husband, Ahmad Zulkarnain Rosli, thanks for your do’as,

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ABSTRACT

The high penetration level of distributed generation (DG) provides numerous potential environmental benefits, such as high reliability, efficiency, and low carbon emissions. However, the effective detection of islanding and rapid DG disconnection is essential to prevent safety problems and equipment damage caused by the island mode operations of DGs. The common islanding protection technology is based on passive techniques that do not perturb the system but have large nondetection zones. Therefore, the first part of this thesis attempts to develop a simple and effective passive islanding detection method with reference to a probabilistic neural network-based classifier, as well as utilizes the features extracted from three-phase voltages seen at the DG terminal. This approach enables initial features to be obtained using the phase-space technique. This technique analyzes the time series in a higher dimensional space, revealing several hidden features of the original signal. Meanwhile, the second part of the thesis focuses on the development of an optimal load shedding scheme after the system experiences an unintentional islanding state to prevent system collapse due to load-generation mismatch and voltage instability encountered in the islanded part of the system. To handle this optimization problem, a constraint multiobjective function that considers the linear static voltage stability margin and amount of load curtailment was formulated. A novel heuristic optimization technique based on the backtracking search algorithm (BSA) was subsequently proposed as an optimization tool for determining the optimum load shedding based on the proposed objective function. Several test systems, including a radial distribution system with two DG units and the Institute of Electrical and Electronics Engineers (IEEE) 33-bus radial distribution system with four DG units, were utilized to evaluate the effectiveness of the proposed islanding detection method and the optimal load shedding scheme. The effectiveness of the proposed islanding detection method was verified by comparing its results with the conventional wavelet transform (WT)-based technique through intensive simulations conducted with the DIgSILENT Power Factory® software. The assessment indices, namely, the mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE), obtained a 0% error rate for the proposed method when applied to the IEEE 33-bus radial distribution system with four DG units. Meanwhile, the MAPE,

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ABSTRAK

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CHAPTER II LITERATURE REVIEW

2.1 Islanding Detection Methods 8

2.1.1 Central Islanding Detection Techniques 9 2.1.2 Review of the Conventional Local Islanding Detection 12

Technique

2.1.3 Review of the Intelligent Local Islanding Detection 16 Technique

2.2 DG Models for Islanding Detection 23

2.3 Load Shedding Schemes 25

2.3.1 Review of Under-Frequency Load Shedding Schemes 26 2.3.2 Review of Under-Voltage Load Shedding Schemes 29

2.4 DG Models for Optimal Load Shedding 33

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2.6 Chapter Summary 36

CHAPTER III ISLANDING DETECTION USING PHASE-SPACE AND NEURAL NETWORK

3.1 Introduction 38

3.2 Tools and Methods used in the Proposed Method 39

3.2.1 Phase-Space Technique 39

3.2.2 Probabilistic Neural Network 41

3.3 Proposed Islanding Detection Method 44

3.3.1 Data Collection 44

3.3.2 Phase-Space Feature Extraction 45

3.3.3 Design of Artificial Intelligent Classifier 47

3.4 Performance Evaluation Methods 52

3.4.1 Performance Evaluation of Conventional Method 52 3.4.2 Performance Evaluation with Statistical Indices 54

CHAPTER IV OPTIMAL LOAD SHEDDING SCHEME USING BACKTRACKING SEARCH ALGORITHM

4.1 Introduction 56

4.2 Tools and Methods Used in Proposed Load Shedding Scheme 56

4.2.1 Voltage Stability Margin 56

4.2.2 Backtracking Search Optimization Algorithm 58 4.2.3 Newton–Raphson Power Flow Solution 61 4.3 Problem Formulation for Optimal Load Shedding Scheme 62

4.3.1 Operational Constraints 63

4.3.2 Fitness Function 64

4.3.3 Application of BSA for Optimal Load Shedding 65 Scheme

4.4 Performance Evaluation Scheme 69

4.4.1 Performance Evaluation with Conventional GA 69 Method

CHAPTER V RESULTS AND DISCUSSION

5.1 The Test System for Islanding Detection 72

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Units 72

5.1.2 IEEE 33-Bus Radial Distribution System with Four 73 DG Units

5.2 Test Results of the Radial Distribution System with Two 74 Identical DG Units

5.2.1 Input Feature Extraction 76

5.2.2 Results of RBFNN with Phase-Space Features 78 5.2.3 Results of RBFNN with Wavelet Transform Features 79 5.2.4 Results of PNN with Phase-Space Features 80 5.2.5 Results of PNN with Wavelet Transform Features 81 5.2.6 Summary of the Result Obtained for All of the Tested 81

Islanding Detection Methods

5.3 Implementation of Islanding Detection on The IEEE 33-bus 84 Radial Distribution Systems with Four DG Units

5.3.1 Input Features Extraction 85

5.3.2 Results of RBFNN with Phase-Space Features 88 5.3.3 Results of RBFNN with Wavelet Transform Features 89 5.3.4 Results of PNN with Phase-Space Features 90 5.3.5 Results of PNN with Wavelet Transform Features 91 5.3.6 Summary of Islanding Detection Analysis 91 5.4 Description of the Test System for Optimal Load 93

Shedding Scheme

5.5 Test Results of Optimal Load Shedding Scheme 96

5.5.1 Optimal Load Shedding for Island A Using BSA 99 5.5.2 Optimal Load Shedding for Island A Using GA 106 5.5.3 Optimal Load Shedding for Other Islanded Systems 113 5.5.4 Summary of Load Shedding Scheme 124

CHAPTER VI CONCLUSIONS AND RECOMMENDATIONS

6.1 Overall Conclusions 126

6.2 Significant Contributions of the Research 128

6.3 Recommendations for Future Studies 129

REFERENCES 130

APPENDIXES

A IEEE 33-Bus Radial Distribution System 140

B Wavelet Technique as Features Extraction 141

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LIST OF TABLES

Table Number Page

2.1 Summary of remote islanding detection techniques 12 2.2 Comparison of passive islanding detection techniques 13 2.3 Comparison of active islanding detection techniques 15

2.4 Utilization of SP in islanding detection 20

2.5 Utilization of the AI classifier in islanding detection at DG 23 2.6 Comparison of various computational intelligence load

shedding schemes

33

3.1 Classifier output definition 45

3.2 Selected phase-space features 45

3.3 A scale of judgment of forecasting accuracy 55

5.1 System model description 72

5.2 DG installed node with operating points 74

5.3 Number of samples for training and testing 76

5.4 Parameter settings of the RBFNN and PNN classifiers for DG1 and DG2

77 5.5 RBFNN classification results with phase-space features 79 5.6 RBFNN classification results with wavelet transform

features

80 5.7 PNN classification results with phase-space features 81 5.8 PNN classification results with wavelet transform features 81 5.9 Comparison of PNN classifier performance with phase-space

and wavelet features

82 5.10 Comparison of MAPE, RMSE, and MAE for various

islanding detection methods

83 5.11 Number of samples for training and testing at IEEE 33 bus

system

85 5.12 Parameter settings of the RBFNN and PNN classifiers for

DG1, DG2, DG3, and DG4

88 5.13 RBFNN classification results with phase-space features 88 5.14 RBFNN classification results with wavelet transform

features

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5.16 PNN classification results with wavelet transform features 91 5.17 Comparison of PNN classifier performance with phase-space

and wavelet feature

92 5.18 Comparison of MAPE, RMSE, and MAE for various

islanding detection methods

92

5.19 Rated maximum power of DGs 93

5.20 Percentage load priority limits for the IEEE 33-bus radial distribution system

94 5.21 Overall power demand and supply in islanded system 96

5.22 GA and BSA parameter settings 96

5.23 Amount of hourly load curtailment at individual buses in island A

103 5.24 Summary of load shedding performances at hour 9.00 113 5.25 Summary of load shedding performances at hour 15.00 117 5.26 Amount of hourly load curtailment at individual buses in

island B

121 5.27 Amount of hourly load curtailment at individual buses in

island C

122 5.28 Amount of hourly load curtailment at individual buses in

island D

123 5.29 Performance of BSA and GA in terms of fitness, VSM, and

amount of load curtailment at hour 9.00

124 5.30 Performance of BSA and GA in terms of fitness, VSM, and

amount of load curtailment at hour 15.00

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LIST OF FIGURES

Figure Number Page

1.1 (a) Traditional distribution system, (b) generation embedded distribution system

2

1.2 Power islanding condition 2

1.3 Voltage and frequency response 3

2.1 Classification of islanding detection technique 9

2.2 Remote islanding detection technique 9

2.3 Transfer trip scheme 11

2.4 Basic block of intelligent islanding detection technique and classification

16

2.5 Block diagram of the inverter 24

2.6 Schematic diagram of mini hydro power for grid connected operation

24

2.7 Types of load shedding schemes 25

2.8 Generator model 34

2.9 System for load shedding 35

2.10 Simplified illustration of the concept behind three types of power distribution configuration

36

3.1 Architecture of a PNN 43

3.2 Phase-space feature extraction, (a) three-phase fault, (b) Euclidean norm (Ex) of the fault signal in (a), (c) selected region of Euclidean norm (Ex) for feature selections, and (d) Euclidean norm (Ex) and its features of the fault signal in (a)

47

3.3 Summary of phase-space based classifier 48

3.4 Implementation steps of phase-space-based islanding detection scheme

50 3.5 Pseudo-code for phase-space-based islanding detection

scheme

51 3.6 Implementation steps of wavelet transform-based islanding

detection scheme

53

4.1 Typical radial feeder of distribution system 57

4.2 General flow chart of BSA 60

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4.4 Pseudo-code for optimal load shedding scheme using BSA 69

4.5 Optimal load shedding scheme using GA 70

5.1 Power distribution systems with two identical DG units 73 5.2 Single-line diagram of IEEE 33-bus radial distribution

system with four DG units

74 5.3 Possible islands and NDZ regions in the radial distribution

system with two DG units

75 5.4 Samples of selected phase-space features for islanding and

non-islanding events at DG1 and DG2 in the studied system, (a) grid disconnection events (islanding condition), (b) capacitor switching events (non-islanding condition), and (c) three-phase fault event (non-islanding condition)

78

5.5 Regression analyses of RBFNN-based DG1 and DG2 classifier using phase-space features

79 5.6 Regression analyses of RBFNN-based DG1 and DG2

classifier using wavelet transform features

80 5.7 Possible islands and NDZ region in the IEEE 33-bus radial

distribution system with four DG units

84 5.8 Samples of selected phase-space features for islanding and

non-islanding events at DG1, DG2, DG3, and DG4 in the studied system, (a) grid disconnection events (islanding condition), (b) capacitor switching events (non-islanding

condition), and (c) three-phase fault event (non- islanding condition)

87

5.9 Regression analyses of RBFNN-based DG1, DG2, DG3, and DG4 classifier using phase-space features

89 5.10 Regression analyses of RBFNN-based DG1, DG2, DG3,

and DG4 classifier using wavelet transform features

90

5.11 Hourly load profile for individual loads 94

5.12 Hourly PV power production: (a) DG1 (b) DG3 95

5.13 Single line diagram of islanded systems, (a) power island A, (b) power island B, (c) power island C, and (d) power island D

98

5.14 Daily load profile and power generation for island A 99 5.15 Proposed load shedding scheme performance for island (a)

generation and load mismatch (b) optimum load profile with load priority limits

100

5.16 Performance of proposed load shedding scheme at hour 9.00 for island A (a) convergence characteristic, (b) individual load demand after optimization, and (c) voltage profile

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5.17 Performance of proposed load shedding scheme at hour 15.00 for island A (a) individual load demand after optimization and(b) voltage profile

106

5.18 Performance of load shedding scheme at hour 9.00 for island A (a) individual load demand after optimization

using GA, (b) comparison of individual load demand after optimization between BSA and GA, and (c)

comparison of voltage profiles obtained using BSA and GA

108

5.19 Performance of load shedding scheme at hour 15.00 for island A (a) individual load demand after optimization using GA, (b) comparison of individual load demand after optimization between BSA and GA, and (c) comparison of voltage profiles obtained using BSA and GA

110

5.20 Performance comparison of GA and BSA in obtaining optimal load shedding in island A at hour 9.00

111 5.21 Performance comparison of GA and BSA in obtaining

optimal load shedding in island A at hour 15.00

112 5.22 Comparison of individual load demand after optimization

by BSA and GA at hour 9.00 for (a) Power island B, (b) Power island C, and (c) Power island D

115

5.23 Comparison of voltage profile before and after load shedding at hour 9.00 for (a) Power island B, (b) Power island C, and (c) Power island D

116

5.24 Comparison of individual load demand after optimization between BSA and GA at hour 15.00 for (a) Power island B, (b) Power island C, and (c) Power island D

118

5.25 Comparison of voltage profile before and after load shedding at hour 15.00 for (a) Power island B, (b) Power island C, and (c) Power island D

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LIST OF ABBREVIATIONS

AFD Active Frequency Drift AI Artificial Intelligent

ANFIS Adaptive Neuro-Fuzzy Interference System ANN Artificial Neural Network

AVR Automatic Voltage Regulator

BSA Backtracking Search Optimization Algorithm

CCP Common Coupling Point

CF Correlation Factor

CWT Continuous Wavelet Transform DG Distributed Generation

DMS Distribution Management System

DT Decision Tree

DWT Discrete Wavelet Transform FCM Frequency Calculator Module FFT Fast Fourier Transforms

FL Fuzzy Logic

FLC Fuzzy Logic Control

GA Genetic Algorithm

IM-SMS Improved- Slip-Mode Frequency Shift LSCM Load Shed Controller Module

MAE Mean Absolute Deviation

MAPE Mean Absolute Percent Error

MFs Membership Functions

MG Microgrid

MPPT Maximum Power Point Tracker MRA Multi-Resolution Analysis

NDZ Non-Detection Zones

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PV Photovoltaics

RBFNN Radial Basis Neural Network

RMSE Root Mean Squared Error

ROCOF Rate of Change of Frequency ROCOV Rate of Change of Voltage

SCADA Supervisory Control and Data Acquisition SFS Sandia Frequency Shift

SMS Slip-Mode Frequency Shift

SP Signal Processing

SVM Support Vector Machines SVS Sandia Voltage Shift

T&D Transmission and Distribution TFD Time-Frequency Distribution THD Total Harmonic Distortion UFLS Under Frequency Load Shedding UFP/OFP Under/Over Frequency Protection UVLS Under Voltage Load Shedding UVP/OVP Under/Over Voltage Protection VSC Voltage-Source Control

VSM Voltage Stability Margin

VU Voltage Unbalance

WPT Wavelet Packet Transform

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LIST OF SYMBOLS

 Euclidean Space

i

ˆ

x Column Vector

(r) bus

S

 Residual Power

& AND Operator

* Complex Conjugate

:= Update Operation

[P (x|y)] Probability of Event x while Event y Given [P (y)] Overall Probability of All Events y

[P (y|x)] Probability of Event y while Event x Given

|| OR Operator

∆P Power Imbalance

a and b Random Number Between 0 to 1

d First Order Differential Equation

D Dimension

dc Correlation Dimension

∂f/∂t rate of Change of Frequency (Hz/s).

diag(Ebus) Diagonal Bus Voltage Matrix Ebus Bus Voltage Matrix

Ebus* Complex Conjugate of the Bus Voltage Vector

Ex Euclidean Norm

f Nominal Frequency (Hz)

F Algorithm Dependent Parameter Utilized to Control the Amplitude of the Search-Direction

f Fitness Function

H Inertia Constant of Generator

h Priori Probability of Patterns being in Category A or B

hA, hB, nA, and

nB

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Hz Hertz

Ibus Bus Current Matrix

J Jacobian Matrix in Complex Form

k Number of Feeders in The System

kV Kilovolts

kW Kilowatts

Lfactor Load Shedding Vector

Li Loading Index

ms Milisecond

MVA Mega Volt-Ampere

MVar Mega Volt-Ampere reactive

MW Mega Watt

n Number of Sample Points

Ns Sampling Rate in Each Period

oldP Historical Population

P Population

P (x) Probability of Event x

Pdi Active Power Consumed by the Load Pe Electric Power in Generator

Pgen Generator Power

Pgi Generated Active Power

Pi Real Power Entering Bus i

Pij jth individual element in the problem dimension that falls in ith position in a population dimension

Ploss Active Power Losses in the Network

Pm Prime Mover Output Power

Pmi Prime Mover Input Power

Premaining load Total Remaining Load

Qdi Reactive Power Consumed by the Load

Qgi Generated Reactive Power

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Qloss Reactive Power Losses in the Network

rand Random Value Obtained from a Standard Normal Distribution

s Second

Sbus Vector of Bus Complex Power Vector

Sl Apparent Power

Sl-i Value of Remaining Load Power

Sl-max Maximum Thermal Limit

Spriority Priority Load Limit

T Trial Population

U Uniform Distribution

up and low Upper and Lower Boundaries Vdc Direct Current Voltage

Vi-max Maximum Permissible Voltage at Bus i

Vi-min Minimum Permissible Value of the Voltage at Bus i

Vk Voltage at Bus k

Vm Voltage at Bus m

VSMsys Overall System Voltage Stability Margin

xi Row Vector

Xi Original Data

Xi ^

Forecasting Data

xwi Weight Input of xi to Neuron

Ybus Element of the Bus Admittance Matrix

Ybus* Complex Conjugate of the Bus Admittance Matrix

Ydgi Outputs of the Individual Classifier

Youtput Final Output of the Decision Making

δkm Angle Between Bus k and Bus m

θ (a) Heaviside Step Function Smoothing Parameter Time Delayed

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CHAPTER I

INTRODUCTION

1.1 RESEARCH BACKGROUND

Traditionally, electrical energy in the distribution system is always supplied to the customer from upstream power resources that are connected to the bulk transmission system. A small localized power source called distributed generation (DG) becomes an alternative to bulk electric power generation due to yearly demand growth. These DGs include wind farms, micro hydro turbines, photovoltaics (PV), and other generators. These DGs are generally in the range of a few kWs up to a few MWs and have several advantages, such as environmental benefits, improved reliability, increased efficiency, prevention of transmission and distribution (T&D) capacity upgrades, improved power quality, and reduced T&D line losses (Balaguer-álvarez et al. 2010; Ray et al. 2011). Figure 1.1 shows the difference between traditional and multiple embedded distribution systems, in which additional DG is commonly connected near the local load compared with the traditional network system. Therefore, the traditional approach of energy production and distribution are changing, introducing new challenges in balancing the power system.

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(a) (b)

Distribution External Grid

consumers consumers consumers

consumers

consumers consumers

DG DG

DG Distribution

External Grid

consumers consumers consumers

consumers

consumers consumers

Figure 1.1 (a) Traditional distribution system, (b) generation embedded distribution system

Network Load Utility

Tripped Utility Circuit Breaker

DG Industrial Site Power

Island

Local Load

Figure 1.2 Power islanding condition Sources: Ezzt et al. 2007

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Voltage drops to balance reactive power Frequency reduces to balance active power

Transient period Steady-state islanded Steady-state

Grid-connected

Frequency Voltage

Governor boosts power output AVR boots

excitation current Voltage stabilised

by generator field voltage

Fault occurs, initiating island

Time

F

re

q

u

en

cy

/V

o

lt

ag

e

Figure 1.3 Voltage and frequency response Source: Ecconnect 2001

Generally, automatic load shedding has two types. The first type is under-frequency load shedding (UFLS), which is designed to rebalance load and generation within an electrical island once the unbalanced system is created. The second type is under-voltage load shedding (UVLS), which is utilized to prevent local area voltage collapse and to directly respond to the voltage condition in a local area. The UVLS scheme aims to shed load to restore reactive power relative to demand, to prevent voltage collapse, and to contain a voltage problem within a local area rather than allowing it to spread in geography and magnitude.

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1.2 PROBLEM STATEMENT

Although the islanding operation has some benefits, several drawbacks are still observed, especially in unintentional islanding events. The unintentional islanding of DGs may cause problems in terms of power quality, safety, voltage and frequency stability, and interference (Mahat et al. 2011; Timbus et al. 2010). The Institute of Electrical and Electronics Engineers (IEEE) 1547-2003 standard specifies a maximum delay of 2 s for the detection of the unintentional islanding condition, whereas the IEEE 929-2000 standard requires the disconnection of the DG if islanded (Mahat et al. 2011). To achieve this goal, each DG must be capable of detecting the islanding condition as quickly as possible. Therefore, the first part of the current study attempts to develop a simple and effective method that can quickly diagnose the islanding condition by identifying the islanding and non-islanding conditions in the system.

Several techniques have been developed to accurately identify the islanding condition; the most economical and effective technique is to use a passive technique with the application of artificial intelligence (AI). This technique is preferred because a more accurate online detection is required to monitor the condition of the system. Moreover, this technique is usually less complex and has high computational efficiency with good accuracy and reliability. The most common technique being used nowadays is the combination of signal processing (SP) and neural network.

Islanding detection and classification and load shedding scheme for dispersed generation integrated radial distribution systems.