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研究生: Netravati Gundalli
Netravati Gundalli
論文名稱: Modified Adaptive Differential Evolution for Optimal Reconfiguration and Distributed Generator Allocation in Distribution Network
Modified Adaptive Differential Evolution for Optimal Reconfiguration and Distributed Generator Allocation in Distribution Network
指導教授: 郭政謙
Cheng-Chien Kuo
口試委員: Hung-Cheng Chen
Hung-Cheng Chen
Chun-Yao Lee
Chun-Yao Lee
Hong-Chan Chong
Hong-Chan Chong
郭政謙
Cheng-Chien Kuo
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 79
中文關鍵詞: Distributed generationDistribution system reconfigurationlinear population size reduction technique of success history based adaptive differential evolution
外文關鍵詞: Distributed generation, Distribution system reconfiguration, linear population size reduction technique of success history based adaptive differential evolution
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  • Reconfiguration is an indispensable method for loss reduction in power distribution systems and is also used to restore loads in out-of-service areas in case of a fault. Power loss in an electrical distribution network is unavoidable. In order to have an efficient and economical operation, the power loss can be minimized up to some level. This thesis presents an efficient way of solving distribution system reconfiguration (DSR). The objective of the thesis is to minimize the active/real power loss, bus voltage profile improvement and boost capacity of the system by simultaneous reconfiguration and DG allocation in radial distribution networks.
    Usually, the distribution network is a closed loop even though the operation is radial with the opening of a unique sectionalizing switch that disconnects a branch/line in the loop. This process of reconfiguration is done in a way such that system loss is minimized. The additional and effective way of reducing power loss is done by distributed generators (DGs) to the system buses. A great number of researchers have been proposed for distributed generation (DG) placement in distribution networks to minimize the power loss. Very few researchers have been done for reconfiguration in parallel with the DG installation for the minimization of system power loss.
    In this thesis work, linear population size reduction technique of success history based adaptive differential evolution (L-SHADE) is used for the simultaneous reconfiguration and DG allocation. The results obtained from the application of the proposed method on two well-known distribution networks such as IEEE 33-bus and IEEE 69-bus radial distribution system. The simulation results demonstrate that the L-SHADE method is able to find highly competitive results when compared with the other literature.


    Reconfiguration is an indispensable method for loss reduction in power distribution systems and is also used to restore loads in out-of-service areas in case of a fault. Power loss in an electrical distribution network is unavoidable. In order to have an efficient and economical operation, the power loss can be minimized up to some level. This thesis presents an efficient way of solving distribution system reconfiguration (DSR). The objective of the thesis is to minimize the active/real power loss, bus voltage profile improvement and boost capacity of the system by simultaneous reconfiguration and DG allocation in radial distribution networks.
    Usually, the distribution network is a closed loop even though the operation is radial with the opening of a unique sectionalizing switch that disconnects a branch/line in the loop. This process of reconfiguration is done in a way such that system loss is minimized. The additional and effective way of reducing power loss is done by distributed generators (DGs) to the system buses. A great number of researchers have been proposed for distributed generation (DG) placement in distribution networks to minimize the power loss. Very few researchers have been done for reconfiguration in parallel with the DG installation for the minimization of system power loss.
    In this thesis work, linear population size reduction technique of success history based adaptive differential evolution (L-SHADE) is used for the simultaneous reconfiguration and DG allocation. The results obtained from the application of the proposed method on two well-known distribution networks such as IEEE 33-bus and IEEE 69-bus radial distribution system. The simulation results demonstrate that the L-SHADE method is able to find highly competitive results when compared with the other literature.

    Abstract i Acknowledgements ii List of Figures v List of Tables vi List of Symbols vii Chapter 1 1 Introduction 1 1.1. Background 1 1.2. Literature Review 3 1.3. Optimization, Feasibility and Constraints 5 1.4. The Scope of the Study 6 1.5. Objectives 7 1.6. Applications and Benefits of Optimal Reconfiguration 7 1.7. Thesis Organization 8 Chapter 2 9 Power Flow and Optimization Techniques 9 2.1. Power Flow 9 2.2. Power Flow Solution Techniques 9 2.3. Distribution Generation 11 2.4. Reconfiguration 13 2.5. Optimization Algorithms for the Power Flow Problems 13 2.6. Some Metaheuristic Algorithms Approaches to Optimization 14 2.7. Differential Evolution (DE) Algorithm and Related Works 19 Chapter 3 25 Methodology 25 3.1. Mathematical Model 25 3.2. Modified L-SHADE Algorithm 28 3.3. MATPOWER 38 Chapter 4 42 Data and Results 42 4.1. 33-Bus Test System 42 4.2. Scenarios for the 33-Bus Test System 42 4.3. 69-Bus Test System 51 4.4. Scenarios for the 69-Bus Test System 51 Chapter 5 59 Conclusions 59 5.1. Conclusions 59 5.2. Scope for Future work 59 Appendix A 60 Data 60 1. IEEE 33-Bus Test System 60 2. IEEE 69- Bus Test System 62 References 65

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