臺灣博碩士論文加值系統

本論文提出量子二進制粒子群演算法，應用於協助智慧電網重要客戶之復電轉供規劃，其研究重點在於操作開關之動作狀態資訊，並配合負載優先性之選擇條件，整合建立出復電策略之問題數學模型，再輔以量子二進制粒子群演算法，以有效計算最佳轉供路徑，同時確保停電區域最小化及操作開關次數最少。此量子二進制粒子群演算法之提出，乃根據二進制粒子群演算法並結合量子計算之概念，藉由量子位元和狀態的疊加，取代了原本粒子群速度更新之過程，進而改善粒子群會過早收斂於區域解的問題，此外，此法亦結合量子理論與粒子群理論的優點，適合解決組合和最佳化的問題。為驗證此演算法於重要客戶之復電策略效能，本論文與其他演算法進行分析比較，並由不同系統測試結果可得知，此量子二進制粒子群演算法應用於智慧電網重要客戶之復電策略擬定上，確實有較佳的計算和收斂效能，並已兼具延伸應用至電力系統相關議題之發展潛力。

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
ABSTRACT ii
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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