||A Study of Power System Operation Enhancement and Protection Decision Support Using Intelligent Computation Technology and Integration
||Department of Electrical Engineering
fault section estimation
plant growth-based optimization algorithm
enhanced honey-bee mating optimization algorithm
Following the increasing concern on the limited energy resources as well as environmen-tal enduring capability, power utilities around the world are being focused on mitigating carbon emissions and achieving a low-carbon society. Therefore, the improve¬ment of power transmission and distribution and the enhancement of power automation are constantly to be important concerns for electric power company, anticipating that a high-quality, high-efficiency, environmental-friendly and user-demand-oriented power grids will be reached. Among several strategies which have been proposed and conducted, the capacitors are usually suggested to be implemented with connected feeders in most engi-neering practices since they are beneficial to increase the power factor and improve the operation performance. Experience and related technical standards have been enacted as useful guides to determine the types, locations and sizes of capacitor banks. However, if the load duration curve, investment cost, social cost for carbon dioxide emissions and en-ergy losses are further included in the optimization consideration, the complexity of the problem would become significant.
Next, in order to cope with the impact of extreme weather on the stability and security of power delivery, the enhancement of power automation is also a vital task. At present, the dispatchers often employ the statuses of relays and circuit breakers collected from supervi-sory control and data acquisition (SCADA) systems with anticipation to estimate fault sec-tions in the early stage. However, for cases where the relay or circuit breaker fails to oper¬ate or facing the multiple faults as well as under the high stresses in interpreting the volumi-nous alarms, this task of fault section diagnosis can be extremely difficult, mean¬while implying that the development of an effective approach as a decision support to help identify the fault location would have its critical importance.
Being situated under the fast-paced industry and increased concerned of supplying power quality, this dissertation is aimed to enhance the operation performance of power systems and the decision support of power system protection strategies. Both the plant growth-based optimization (PGBO) approach and enhanced honey-bee mating optimiza-tion (e-HMO) algorithm have been developed and realized on real system applications. It is expected that through the extensive study of this dissertation, not only the optimization decision of capacitor placement can be attained through the reduction of system loss along with and the decrement of carbon dioxide emission, but the operators can also justify and isolate the fault sections swiftly such that the affected duration of outage can be minimized while the quality of supplying power can be upgraded. In order to evaluate the contribution of these proposed methods to power system operation, they were evaluated through the equivalent system simulations and practical operation data with comparisons to other meth-ods. Test results gained from this dissertation have confirmed the feasibility of the pro-posed methods to determine the capacitor placement and estimate fault sections in power systems, thereby serving as beneficial reference for planning engineers and opera¬tors and paving a road towards the goal of a clean and robust power network.
List of Tables VIII
List of Figures IX
Symbols and Abbreviations X
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Literature Survey 3
1.3 Contribution of this Dissertation 5
1.4 Organization of this Dissertation 5
Chapter 2 Problem Description 9
2.1 Introduction 9
2.2 Capacitor Placement Problems 9
2.3 Fault Section Estimation Problems 10
2.3.1 Fault Section Estimation Problems Analysis 11
2.3.2 Mathematical Model Formulations of Fault Section Estimation Problems 13
2.4 Overview of Intelligent Computation Technology 15
2.5 Summary 16
Chapter 3 Application of Plant Growth-Based Optimization Algorithm to Capacitor Placement Optimization in Power Systems 18
3.1 Introduction 18
3.2 Overview of Plant Growth-Based Optimization Algorithm 18
3.2.1 Growth mechanism of a plant phototropism 19
3.2.2 Mathematical Model Formulation of PGBO Algorithm 19
3.2.3 PGBO Algorithm Paradigm 22
3.3 Computation Procedures of Plant Growth-Based Optimization Approach 24
3.4 Numerical Studies of Plant Growth-Based Optimization Algorithm 27
3.5 Summary 37
Chapter 4 Estimation of Fault Sections in Power Systems Using Enhanced Honey-Bee Mating Optimization Algorithm 38
4.1 Introduction 38
4.2 Overview of Enhanced Honey-Bee Mating Optimization Algorithm 38
4.2.1 Honey-Bee Mating Optimization 39
4.2.2 Enhanced Honey-bee Mating Optimization Algorithm 41
4.3 Computation Procedures of e-HMO Algorithm for FSE Problems 41
4.4 Test Results of e-HMO Algorithm for FSE Problem 43
4.4.1 Validation of Methods 44
4.4.2 Convergence Test 49
4.4.3 Robustness Test 51
4.4.4 Comparisons of Computation Efficiency 55
4.5 Summary 58
Chapter 5 Conclusions 59
5.1 Conclusions 59
5.2 Future Study 60
Publications List 71
Projects List 73
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