臺灣博碩士論文加值系統

電力系統主要是由發電、輸電、配電三個部份組合而成，其中配電階層是與用戶直接接觸的層級，其所涵蓋的範圍廣泛，遍及各鄉鎮角落以及偏遠山區海邊，線路多且繁雜，所以配電系統規劃設計之良窳，攸關電力公司之供電品質，以及系統穩定度。
根據文獻記載發電階層所發的電力約有百分之十三是消耗在配電階層，而其中因供應負載所需之無效功率，所造成無效電流的流動，是導致饋電線路損失的一個重要因素，所以如何降低饋電線路中無效電流，進而改善匯流排電壓，以達到減少饋電線路損失、改善用戶之供電品質，是一個重要的課題。
電容器是補償無效功率、改善電壓調整率成本最低而且是最有效的方法之一。本論文提出以結合基因演算法與模擬退火法為最佳化方法，求解饋電線路中切換式電容器最佳補償位置（Location）、大小（Size），使得饋電線路的電能損失成本，以及電容器的裝置成本，兩者總和之總成本最低。並以IEEE 69 BUS的饋電線系統模擬，求解線路加裝電容器補償前以及補償後，總成本變化的情況，結果證實結合基因演算法與模擬退火法優於其他最佳化方法，能在短時間內求得令人滿意的收斂值，是解決組合型電容補償問題的有效方法。

Electric power system mainly consists of three parts , which are generation, transmission and distribution. Distribution system has the direct contact with the users ranging from crowded cities to remote countryside. The distribution system is complicated. Therefore, a good planning of distribution system will enhance the power quality and stability of the system.
According to studies, the distribution level takes out 13% of the power generated by the generating level. The main reason of feeder losses is reactive current flow in the distribution line. Thus, it is an important task to reduce reactive current in the feeder, to improve bus voltage, and to reduce the losses of the feeder.
Capacitors are one of the most effective options to compensate reactive power and to improve voltage regulation. The thesis proposes the combination of genetic algorithm and simulated annealing as optimal method to find out the optimal location and size of the switched capacitors in order to reduce the total cost of capacitors and of feeder losses. To find out the changes of cost between before and after capacitor compensated, IEEE 69-bus distribution system was used to test in this thesis. It proves that the combination of genetic algorithm and simulated annealing is the most effective method to solve the combinatorial capacitor compensated problem.

摘要i
Abstractii
誌謝iii
目次iv
圖目錄vii
表目錄ix
第一章 緒論1
1.1研究背景1
1.2 研究目的2
1.3 研究貢獻3
1.4 論文架構4
第二章 問題描述6
2.1前言6
2.2負載消耗功率之計算6
2.2.1電阻性負載6
2.2.2電感性負載7
2.2.3電容性負載8
2.2.4組合性負載8
2.3負載曲線9
2.3.1日負載曲線10
2.3.2負載持續曲線10
2.4電容器類型12
2.4.1固定式電容器12
2.4.2切換式電容器12
2.4.3切換式電容器裝置成本曲線12
2.5負載潮流計算14
2.5.1 匯流排注入電流對分支之電流矩陣（Bus Injection Current to Branch Current Matrix，BICBC）15
2.4.2 分支電流對匯流排電壓矩陣（Branch Current to Bus Voltage Matrix， BCBV）17
2.4.3 矩陣18
2.4.4負載潮流演算法（ Load Flow Algorithm）19
第三章 配電系統與無效電力之補償20
3.1前言20
3.2饋線（Feeder）22
3.2.1饋電線路之型態23
3.2.1.1樹枝狀23
3.2.1.2環路狀25
3.2.1.3兩電源供電方式（Double Radial Power Line Systems ）28
3.3二次配電線路（Secondary Distribution Line）29
3.3.1二次配電線路之型態29
3.3.1.1樹枝狀30
3.3.1.2低壓互聯狀30
3.3.1.3低壓網路狀33
3.4無效電力補償35
3.5無效電力提供者36
3.6電容器之基本原理36
3.7裝設電容器之效益38
3.7.1降低饋電線路損失38
3.7.2提昇電壓39
3.7.3增加系統之供電能力（Release Thermal Capacity）39
3.7.4降低尖峰負載需求41
3.7.5改善功率因數42
第四章 研究方法44
4.1前言44
4.2基因演算法（Genetic Algorithm）簡介44
4.3基因演算法工作原理48
4.3.1族群（Population）49
4.3.2適合度函數（Fitness Function）49
4.3.3選擇與複製（Reproduction）50
4.3.3.1輪盤法（Roulette Wheel Selection）50
4.3.3.2競爭式選擇法（Tournament Selection）52
4.3.4交配（Crossover）53
4.3.4.1單點交配（One Point Crossover ）53
4.3.4.2兩點交配（Two Point Crossover）54
4.3.4.3制式交配（Uniform Crossover）54
4.3.5突變（Mutation）55
4.3.6終止條件55
4.3.7編碼與解碼56
4.4菁英策略56
4.5基因演算法的優點與缺點57
4.6模擬退火法（Simulated Annealing）簡介58
4.6.1波茲曼（Boltzman）機率分布函數59
4.7 Metropolis演算法61
4.8模擬退火法演算法62
4.9結合基因演算法與模擬退火法63
第五章 實例模擬與結果比較67
5.1前言67
5.2目標函數（Objective Function）67
5.3編碼方式70
5.4 69 bus饋電線系統71
5.5基因演算法模擬74
5.6結合基因演算法與模擬退火法模擬79
第六章 結論與未來研究方向86
6.1結論86
6.2未來研究方向86
參考文獻88
作者簡介91

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