本論文提出以免疫系统法/模糊系統法/禁忌演算法及整合人工智慧與進化演算法(包括混沌搜索、基因演算、免疫糸統、模糊系統、禁忌演算法及模糊神經網路法)，求解電力系統運轉最佳化問題(包括短期負載預測與機組排程問題)。在負載預測問題上，本文乃以模糊類神經網路為主體，然後使用混沌搜尋/基因演算法、模糊系統法及模擬退火法來求取模糊類神經網路之各項参數值(包括網路之權值、偏權值、歸屬函數、歸屬函數中之敏感因子及可調鍵結值)以取代原來用來求解網路最佳参數值之倒傳遞法方式。而在機組排程問題上，乃以混沌搜尋，免疫基因演算法及模糊系統法來求得最佳之機組排程工作，首先我們先混合了免疫糸統法與基因演算法成為一個單一演算法(即免疫基因演算法)，然後將混沌搜尋內嵌於其中，最後並以模糊系統法來作為免疫基因演算法中交配率與突變率兩個参數之調整依據，如此一來可以改善原來基因演算法容易陷於過早收斂及運算緩慢之缺點。

An Integrated Chaos Search Genetic Algorithm (CGA) /Fuzzy System (FS), Tabu Search (TS) and Neural Fuzzy Network (NFN) method for load forecasting is presented in this paper. A Fuzzy Hyper-Rectangular Composite Neural Networks (FHRCNNs) was used for the initial load forecasting. Then we used CGAFS and TS to find the optimal solution of the parameters of the FHRCNNs, instead of Back-Propagation (BP). First the CGAFS generates a set of feasible solution parameters and then puts the solution into the TS. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The TS method on the other hand has good local optimal search capabilities. We combined both methods to try and obtain both advantages, and in doing so eliminate the drawback of the traditional ANN training by BP. This thesis presents a hybrid Chaos Search Immune Algorithm (IA)/Genetic Algorithm (GA) and Fuzzy System (FS) method (CIGAFS) for solving short-term thermal generating unit commitment problems (UC). The UC problem involves determining the start-up and shutdown schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve and individual units. We combined IA and GA, then added chaos search and fuzzy system approach in it. Then we used the hybrid system to solve UC. Numerical simulations were carried out using four cases; ten, twenty and thirty thermal units power systems over a 24-hour period.

總目錄
總目錄 ……………………………………………………... 1
圖目錄 ……………………………………………………... 5
表目錄 ……………………………………………………... 7
中文摘要 …………………………………………………... 9
英文摘要 …………………………………………………... 10
第一章 緒論
1.1 研究背景與動機 ……………………………….. 11
1.2 研究目的 ……………………………………….. 16
第二章 電力系統運轉最佳化問題描述
2.1 簡介 ……………………………………………… 18
2.2 短期負載預測 …………………………………… 18
2.2.1 模糊化多維矩形複合式類神經網路 ………. 18
2.2.2 模糊化多維矩形複合式類神經網路之數學架
構 ……………………………………………. 19
2.2.3 FHRCNN 之適應函數 ………………………... 23
2.3 火力機組排程 …………………………………… 24
2.3.1 火力機組排程之基本概念 …………………. 24
2.3.2 目標函數 ……………………………………. 24
2
2.3.3 限制條件 ……………………………………. 25
第三章 本文中所用之方法
3.1 簡介 ………………………………………………. 29
3.2 混沌搜尋法 ……………………………………… 30
3.2.1 混沌搜尋法之原理 …………………………. 30
3.2.2 混沌系統 …………………………………….. 31
3.3 基因演算法 ……………………………………… 32
3.3.1 基因演算法之基本原理 ……………………. 32
3.3.2 基因演算法之步驟 …………………………. 32
3.3.3 基因演算法如何避免陷入區域最佳解之
陷阱 …………………………………………. 40
3.4 禁忌搜尋法 ……………………………………… 41
3.4.1 禁忌搜尋法之基本原理 ……………………. 41
3.4.2 禁忌搜尋法之演算步驟 ……………………. 42
3.5 免疫演算法 ……………………………………… 46
3.5.1 免疫演算法之基本概念 …………………. 46
3.5.2 識別多樣性機理 …………………………. 47
3.6 進化規劃法 ……………………………………… 49
3.6.1 進化規劃法之基本原理 …………………. 49
3.6.2 進化規劃法之進行 ………………………. 49
3
3.7 模擬退火法 ……………………………………… 55
3.7.1 模擬退火法之基本原理 …………………. 55
3.7.2 模擬退火法之執行步驟 …………………. 55
3.8 類神經網路 ………………………………………. 60
3.8.1 類神經網路之基本原理 …………………. 60
3.8.2 類神經網路之基本架構 …………………. 60
3.8.3 類神經網路之演算步驟 …………………. 61
3.9 模糊系統法 ……………………………………… 64
3.9.1 模糊系統法之基本架構 …………………. 64
3.9.2 模糊系統法組成因素 ……………………. 65
第四章 短期負載預測
4.1 簡介 ……………………………………………… 68
4.2 使用FHRCNN 法作負載預測之基礎 …………. 68
4.2.1 FHRCNN 法之各項参數之調整 ………... 68
4.2.2 基因演算法融合混沌搜尋/模糊系統法
及禁忌演算法之計算過程 ………………. 69
4.3 由CGAFSTS-NFN 法所進行的短期負載預測 … 82
4.4 模擬及測試之結果 ……………………………….. 85
第五章 短期火力機組排程
5.1 簡介 ……………………………………………… 99
4
5.2 融合混沌搜尋/模糊系统法而運作之免疫基因演
算法 ………………………………………………. 99
5.2.1 免疫基因演算法之编碼 ………………… 99
5.2.2 抗體族群之多樣性及親和力之計算 …… 101
5.2.3 CIGAFS 之執行步驟 …………………… 103
5.3 使用CIGAFS 法之重要特點 …………………... 114
5.4 模擬及測試之結果 ……………………………… 115
第六章 結論與未來研究方向
6.1 本論文之貢獻 …………………………………… 129
6.2 未來研究方向 …………………………………… 130
参考文獻 ……………………………….…………………. 132
博士班期間所發表的論文 ……………………………... 139
博士班期間所参與的計劃 ……………………………... 143

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