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研究生(外文):Chien-Lin Lu
論文名稱(外文):Application of Quantum Binary Particle Swarm Optimization to Service Restoration of Priority Customers in Smart Grid
指導教授(外文):Ta-Peng Tsao
口試委員(外文):Jen-Hsiang ChouGuan-Chyun HsiehTing-Chia OuWhei-Min Lin
外文關鍵詞:Quantum Binary Particle Swarm OptimizationAlgorithmService Restoration
  • 被引用被引用:1
  • 點閱點閱:194
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  • 下載下載:15
  • 收藏至我的研究室書目清單書目收藏:1

In this thesis, the Quantum Binary Particle Swarm Optimization (QBPSO) is introduced and applied to service restoration of priority customers in the smart grid. This research is emphasized on movement statement data in operating switch and condition added with load priority. The mathematic model is integrately established for restoration strategy issue with QBPSO combined, in order to effectively indicate the optimal restoration path and simultaneously ensure both the area of outrange and the times of switching minimized. As to this proposed QBPSO, it is based on Binary Particle Swarm Optimization and integrated with the concept of quantum computation. Through quantum bit and statement superimposed, instead of the process of original particle method updated, the problem of premature convergence can be improved. Taking advantages of quantum and particle theory, this can properly meet the satisfaction of combination and the optimization. To verify the efficiency of important client''s restoration strategy, this algorithm is compared with others and analyzed the difference. From the testing results in the different system, this QBPSO does present remarkable ability in computation and convergence, especially for essential client''s restoration strategy in smart grid. It also illustrates the development potential of application extended to power system and relative issues.

摘 要 i
誌 謝 iv
目 錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 3
1.3 論文架構 5
第二章 問題描述 6
2.1 前言 6
2.2 輸配電系統之介紹 7
2.2.1輸配電系統之架構 7
2.2.2輸配電系統之狀態與轉換 9
2.2.3輸配電系統之復電 12
2.3 電力潮流分析 15
2.3.1電力潮流方程式 16
2.3.2求解電力潮流的方法 19
2.3.3牛頓-拉福森法求解電力潮流 20
第三章 演算法理論之介紹 25
3.1 基因演算法 25
3.1.1基因演算法之基本概念 25
3.1.2基因演算法執行步驟 27
3.2 粒子群演算法 34
3.2.1粒子群演算法之基本概念 34
3.2.2粒子群演算法執行步驟 39
3.3 二進制粒子群演算法 41
3.3.1二進制粒子群演算法之基本概念 41
3.3.2二進制粒子群演算法執行步驟 42
3.4 量子演算法 44
3.4.1量子演算法之基本概念 44
3.4.2量子演算法的步驟 46
3.5 量子二進制粒子群演算法 51
3.5.1量子二進制粒子群演算法之基本概念 51
3.5.2量子二進制粒子群演算法執行步驟 52
第四章 復電策略之數學模型 55
4.1 復電策略目標函數 55
4.2 復電策略限制條件 57
第五章 模擬結果與討論 59
5.1 測試系統一 59
5.1.1案例1-1 62
5.1.2案例1-2 65
5.1.3案例1-3 68
5.2 測試系統二 71
5.2.1案例2-1 75
5.2.2案例2-2 80
5.2.3案例2-3 85
5.3 測試系統三 90
第六章 結論與未來研究方向 93
6.1 結論 93
6.2 未來研究方向 94
參考文獻 95

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