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系統識別號 U0002-2006200716261200
中文論文名稱 應用免疫演算法於發電機組維修排程之研究
英文論文名稱 Hydro-Thermal Generator Maintenance Scheduling via Immune Algorithm
校院名稱 淡江大學
系所名稱(中) 電機工程學系碩士班
系所名稱(英) Department of Electrical Engineering
學年度 95
學期 2
出版年 96
研究生中文姓名 林融玨
研究生英文姓名 Jung-Chueh Lin
學號 694380196
學位類別 碩士
語文別 中文
口試日期 2007-06-11
論文頁數 66頁
口試委員 指導教授-蕭瑛東
委員-余繁
委員-黃聰亮
委員-黃聰耀
中文關鍵字 免疫演算法  維修排程 
英文關鍵字 Immune Algorithm  Maintenance Scheduling 
學科別分類 學科別應用科學電機及電子
中文摘要 這幾年來台灣地區經濟快速成長,使得用電量急遽增加。同時,加上能源短缺及電廠的興建受到環保意識抬頭受阻,致使備轉容量明顯偏低。因此,發電機組的維修排程計畫就在電力系統運轉規劃上扮演一個很重要的角色。發電機維修排程主要目的乃在於能獲得最大備轉容量與最低發電成本情況下,求得各發電機組之維修順序與時間。

本論文將擬定維修排程計畫,並以均化備轉率為目標函數,考量實際系統的限制條件,如檢修間隔、人力限制、電力平衡等。雖然在以往的研究中,已有許多方法使用在此問題上,例如動態規劃法、整數規劃法及支界法等,但是由於這些方法所需的求解時間會隨機組數的增加呈指數關係成長,因此都比較適合於小規模系統上使用。

在本論文中,我們提出免疫演算法(Immune Algorithm),來求解機組的維修排程問題。免疫演算法是應用抗體及抗原在免疫系統運作模式來求解最佳化問題,其中,抗體及抗原相當於最佳化問題中求解空間的一解和目標函數。利用抗體族群相似程度之關係,避免陷入局部最優解的可能性,使得在求解空間的搜尋過程中,能夠快速收斂且找到全域最佳解,結果顯示,免疫演算法對於機組的維修排程問題而言,應不失為一個很好的分析工具。
英文摘要 Recently, the reason that economics in Taiwan grow quickly makes load demands increase rapidly. In addition, the energy is hard up and the rise of environmental protection makes the difficulties of generating unit system, the spinning reserve is scant obviously. Therefore, the maintenance scheduling plays an important role within the planning of power system operation.

In this thesis, we plan the maintenance scheduling and levelize the spinning reserve rate to be the objective function. We also consider realistic constraints, such as maintenance alternate interval, crew constraints, power balance requirement, etc. Although departed methods such as dynamic programming, integer programming, and branch bound method can solve small scale problems, the rise in execution time of these methods is exponential with the number of generating units.

In this thesis, we render Immune Algorithm to solve the maintenance scheduling problem. Immune Algorithm is used to solve the optimal problem via the operation of the antibody and the antigen in immune system. The antibody is taken as the solution of the optimal problem and the antigen is taken as the objective function of the optimal problem. To prevent the local optimal solution, we can find out the global optimal solution rapidly during searching for solution space by the diversity of antibody populations. The result obtained from analysis proves that Immune Algorithm is a good method for maintenance scheduling problem.
論文目次 誌 謝…………………………...…………..……….…..……..I
中文摘要………………………………...………………..……II
英文摘要……………………………………………..…………...III
目 錄………………………...…………..…………..……..IV
圖目錄.…………………………………………..……...…...VII
表目錄………………………………...………….....…….…IX

第一章、緒 論…………………………...…………..…………1
1.1 研究背景及目的……………………………....…………..……1
1.2 研究貢獻……………………..........................…5
1.4 論文架構………………………………………………...…...…5
第二章、問題描述與限制條件……………………………………7
2.1 簡介…………...………….……………………. ...7
2.2 目標函數……………….………...…………...……... ..7
2.3 限制條件……………….………...…………...…….... 10
2.3.1 維修原則…………….……….....…………...…10
2.3.2 規劃之限制條件………….……….....…………...…10
第三章、免疫演算法 ...15
3.1 演算法之基本概念 ...15
3.2 免疫系統之原理 ...16
3.3 抗體族群之適合度計算... 19
3.4 免疫演算法之執行步驟... 21
第四章、求解方法...25
4.1 啟發法...25
4.2 動態規劃逐次逼近法...27
4.3 免疫演算法...30
4.3.1免疫演算法用於維修排程的求解步驟...31
第五章、演算法模擬與實例分析...36
5.1 簡單四部機組的測試範例…………………………………..36
5.2 台電系統………………………………..…………………....44
5.2.1測試系統…………………………………………………44
5.2.2測試方法…………………………………………………45
5.2.3測試結果與討論…………………………………………48
第六章、結論與未來研究方向…………………….…..………….58
6.1 結論……………………………………………………………58
6.2 未來研究方向…………………………………………………60
參考文獻………………………….………………………………62
圖目錄
圖1-1 民國84-95 年台電系統尖峰負載曲線圖..........................................2
圖1-2 民國84-95 年台電系統備用容量率曲線圖......................................2
圖3-1 免疫系統的示意圖............................................................................18
圖3-2 抗體之編碼.........................................................................................20
圖3-3 抗體間進行單點交換示意圖............................................................23
圖3-4 抗體間進行突變示意圖....................................................................24
圖4-1 啟發法流程圖.....................................................................................26
圖4-2 動態規劃逐次逼近法流程圖............................................................29
圖4-3 應用免疫演算法於維修排程之程式架構圖....................................31
圖4.4 抗體基因模型圖.................................................................................33
圖4-5 免疫演算法用於維修排程的流程圖.................................................35
圖5-1 使用啟發法所得之維修排程圖.........................................................40
圖5-2 使用免疫演算法所得之維修排程圖.................................................40
圖5-3 使用動態規化法所得之維修排程圖.................................................41
圖5-4 各方法的備轉容量變化比較圖.........................................................42
圖5-5 啟發法與免疫演算法的備轉率比較圖.............................................43
圖5-6 台電72 時段尖峰負載圖...................................................................47
圖5-7 動態規劃逐次逼近與免疫演算法維修排程結果之比較.................51

表目錄
表5-1 四部機組特性資料.............................................................................36
表5-2 四部機組的十二期負載資料.............................................................37
表5-3 使用啟發法求解維修排程之結果.....................................................38
表5-4 使用免疫演算法求解維修排程的結果.............................................39
表5-5 使用動態規劃逐次逼近法求解維修排程的結果.............................39
表5-6 三種方法均化備轉率指標及運轉成本比較.....................................41
表5-7 啟發法與免疫演算法的備轉容量及備轉率比較.............................43
表5-8 台電33 部機組特性資料...................................................................46
表5-9 台電72 時段尖峰負載資料...............................................................47
表5-10 動態規劃逐次逼近法對不同初解最佳化結果比較(案例二)....50
表5-11 免疫演算法對不同初解最佳化結果比較(案例二) ...................50
表5-12 動態規劃逐次逼近法對不同初解最佳化結果比較(案例一)…53
表5-13 免疫演算法對不同初解最佳化結果比較(案例一) ...................54
表5-14 案例一與案例二最佳化結果比較...................................................54
表5-15 模擬台電33 部機的維修排程結果(案例二的初解一) .............55
表5-16 模擬台電33 部機的維修排程結果(案例二的初解二) .............56
表5-17 模擬台電33 部機的維修排程結果(案例二的初解三) .............57


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