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研究生:曾議德
研究生(外文):Yi-te Tzeng
論文名稱:改良之多代理人粒子群體最佳化方法應用於配電系統最小損耗重構
論文名稱(外文):Loss-minimized Distribution System Reconfiguration by Using Improved Multi-agent Based Particle Swarm Optimization
指導教授:楊宏澤楊宏澤引用關係
指導教授(外文):Hong-tzer Yang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:77
中文關鍵詞:粒子群體最佳化多代理人系統雜湊技術降低損耗配電系統重構
外文關鍵詞:Loss ReductionDistribution System ReconfigurationHashing MethodMulti-agent SystemParticle Swarm Optimization
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在配電系統內,可以藉由改變其網路架構以降低其因線阻所造成之系統損耗。網路重構乃藉著啟閉配電系統內各個開關不同的組合來實現。但藉著網路重構以降低系統損耗是一個複雜的最佳化問題,其包含了因非線性方程式所產生的多重區域最佳解以及配電系統上的多項限制等問題。
一個典型的配電系統通常會具備大量的開關,因此藉著分析所有可能的開關組合,以求得最佳解是不切實際的。由於配電系統須保持輻射狀架構以及開關狀態為不連續的性質,使得傳統最佳化技術應用於此網路重構問題上會有若干的困難。因此在文獻中大部分應用於此問題之研究多使用啟發式演算法,藉由分析或是基於經驗發展出之方法。啟發式演算法雖然能夠快速的找出問題的解,但在某些問題上啟發式演算法只能夠搜尋到區域最佳解而無法找到全域最佳解。故本論文乃提出ㄧ最佳化方法應用於此網路重構問題。
本研究使用並改善多代理人粒子群體最佳化之方法,以應用於配電系統最小損耗重構之問題。此一隨機搜尋之最佳化技術,整合了粒子群體最佳化及多代理人系統,以改善傳統粒子群體最佳化尋找最佳解之效率。本文中為進一步提升此演算法之效率並加入雜湊(hashing)技術,以避免重複之電力潮流運算。所提方法測試於一33匯流排之測試系統,並與現有之啟發式演算法以及傳統之粒子群體最佳化方法作比較,以驗證本文所提方法之有效性。
In a distribution system, the system loss caused by line resistance can be minimized by changing its network configuration. For minimizing system loss, reconfiguration of the network is performed by opening or closing switches in the distribution system for different combination of switching status. The network reconfiguration for minimizing system loss is a complicated combinatorial optimization problem, involving nonlinear function with multiple local optima and lots of constraints to be observed.
Since a typical distribution system may have a huge amount of switches, an exhaustive analysis of all possible combinations is not practical. The radial structure and the discrete nature of the switch values make the use of classical optimization techniques impractical to solve the reconfiguration problem. Therefore, most of the algorithms to solve the problem in the literature are based on heuristic search techniques, using either analytical or knowledge-based ways. Heuristic algorithms could find approximate solutions in the literatures; nevertheless they may not find the real optimal solution. As a result, an optimization approach is presented in the thesis for the system reconfiguration problem.
A stochastic optimization technique of particle swarm optimization (PSO) based on the multi-agent system (MAS) is introduced and improved to solve the distribution system reconfiguration problem. The proposed multiagent-based particle swarm optimization (MAPSO) method for the discrete reconfiguration problem integrates the MAS and PSO to improve the efficiency of original PSO method in searching the optimal solution. For further enhancing the efficiency of the algorithm, the hashing scheme is proposed further to eliminate the repeated power flow analyses. The proposed MAPSO for the distribution system reconfiguration problem will be verified in a 33-bus test system and compared with current heuristic methods and the traditional PSO to demonstrate its effectiveness.
摘要............................................................................................................I
Abstract ................................................................................................... II
誌謝.........................................................................................................IV
Contents...................................................................................................V
List of Tables ....................................................................................... VIII
List of Figures .........................................................................................X
Chapter 1 Introduction ...........................................................................1
1-1 Research Motivations..............................................................1
1-2 Review of Literatures..............................................................2
1-3 Research Method.....................................................................4
1-4 Organization of the Thesis......................................................6
Chapter 2 Problem Formulation ...........................................................7
2-1 Introduction .............................................................................7
2-2 Distribution System.................................................................7
2-3 Distribution Automation....................................................... 11
2-4 Minimization of Distribution System Loss .........................13
2-5 Summary................................................................................17
Chapter 3 MAPSO based Optimization Method ...............................18
3-1 Introduction ...........................................................................18
3-2 Particle Swarm Optimization (PSO) ...................................18
3-3 Multi-agent System (MAS)...................................................20
3-4 Integration of PSO and MAS...............................................21
3-5 Summary................................................................................26
Chapter 4 The Proposed MAPSO Approach for Distribution System
Reconfiguration .....................................................................................27
4-1 Introduction ...........................................................................27
4-2 Structure of the Proposed MAPSO Method.......................28
4-3 Feasibility Checking and Correcting...................................30
4-4 Hashing Method ....................................................................37
4-5 Power Flow Analysis for System Loss Estimation .............39
4-6 Computational Equations of the Proposed MAPSO .........44
4-7 Summary................................................................................46
Chapter 5 Simulation Results...............................................................48
5-1 Introduction ...........................................................................48
5-2 Test System.............................................................................48
5-3 Numerical Results .................................................................52
5-3-1 MAPSO without hashing method integrated ..................53
5-3-2 MAPSO with Hashing Scheme Integrated......................60
5-3-3 MAPSO with Hashing Scheme Integrated for Different
Hashing Functions.....................................................................63
5-4 Discussions .............................................................................66
5-5 Summary................................................................................68
Chapter 6 Conclusions and Future Prospects ....................................70
6-1 Conclusions ............................................................................70
6-2 Future Prospects....................................................................71
Reference ................................................................................................72
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