電力工業重構，創造了用戶減少電費的許多機會。在競爭的電力市場中，為了建立用戶的零售選擇權，必須了解不同用戶類別負載曲線的資訊。以目前來說，低壓用戶為了選擇電力供應者而裝設智慧型電錶量測每一小時負載是不切實際的。為了使電力經營者可以容易將用戶歸類到特定的用戶類別的負載曲線並付與確定指數，如何依據用戶負載調查資料和每月用電量，針對用戶所歸類的類別做初步的辨別是市場運作所必須的。
配電系統饋線供電給不同類別的用戶，由於欠缺配電變壓器負載實測資料，用來作為系統分析的資料具有不同程度的不確定性。在目前許多的研究中，模糊負載模型曾被用來表示需量的不確定性，然而許多先前的研究並沒有說明如何推導模糊負載模型。為解決這個問題，提出如何建立模糊負載模型是許多配電網路分析所需要的。
在自由化的市場中，負載整合允許用戶以較低的電價購電。在一些購買合約中，負載因數是負載整合中具有關鍵性的因素。在配電系統中，為建立較佳的負載整合以獲得有利的議價機會，饋線重組可以被用來改善配電子系統的負載因數。
針對上述三個問題，在本論文應用二種資料挖掘的方法，也就是利用模糊 c 平均法（fuzzy c-means）和類神經網路（Artificial Neural Network）做為負載模式辨別的基礎，來進行用戶負載曲線的建立和用戶類別的指派。不同於以前的用戶負載曲線建立技術，我們利用可能性與隨機性之一致性原理（possibility–probability consistency principle），利用負載調查得到用戶每小時負載的機率密度分佈並將其轉換成模糊歸屬函數。其次利用用戶類別模糊負載曲線模型、用戶每月用電量和饋線負載量測值，饋線上各節點變壓器每小時之負載可以被估計並利用在配電網路分析之上。最後我們依據二進位粒子群最佳化（binary particle swarm optimization）技術，利用饋線重組以改善饋線的負載因數，以獲得最佳的電費議價空間。依據在幾個簡單的配電網路上之測試結果得知，二進位粒子群最佳化法可以有效地求解饋線重組的問題。

Power industry restructuring has created many opportunities for customers to reduce their electricity bills. In order to facilitate the retail choice in a competitive power market, the knowledge of hourly load shape by customer class is necessary. Requiring a meter as a prerequisite for lower voltage customers to choose a power supplier is not considered practical at the present time. In order to be used by Energy Service Provider (ESP) to assign customers to specific load profiles with certainty factors, a technique which bases on load research and customers’ monthly energy usage data for a preliminary screening of customer load profiles is required.
Distribution systems supply electricity to different mixtures of customers, due to lack of field measurements, load point data used in distribution network studies have various degrees of uncertainties. In order to take the expected uncertainties in the demand into account, many previous methods have used fuzzy load models in their studies. However, the issue of deriving these models has not been discussed. To address this issue, an approach for building these fuzzy load models is needed.
Load aggregation allows customers to purchase electricity at a lower price. In some contracts, load factor is considered as one critical aspect of aggregation. To facilitate a better load aggregation in distribution networks, feeder reconfiguration could be used to improve the load factor in a distribution subsystem.
To solve the aforementioned problems, two data mining techniques, namely, the fuzzy c-means (FCM) method and an Artificial Neural Network (ANN) based pattern recognition technique, are proposed for load profiling and customer class assignment. A variant to the previous load profiling technique, customer hourly load distributions obtained from load research can be converted to fuzzy membership functions based on a possibility–probability consistency principle. With the customer class fuzzy load profiles, customer monthly power consumption and feeder load measurements, hourly loads of each distribution transformer on the feeder can be estimated and used in distribution network analysis. After feeder models are established, feeder reconfiguration based on binary particle swarm optimization (BPSO) technique is used to improve feeder load factors. Test results based on several simple sample networks have shown that the proposed feeder reconfiguration method could improve customers’ position for a good bargain in electricity service.

中文摘要..………….…………………………………………………...……I
英文摘要……...………………………………………….………………...III
目錄 ……………….………………………………………..………...……V
圖目錄 ……………….………………………...…………………...……VII
表目錄 ……………….…………….………………………………...……IX
第一章 緒論………………….…………………………….………………1
1-1 研究背景與目標…………………...……………..……..………1
1-2 文獻回顧………….……………….…………...………………14
1-3 研究內容…….………………………………………….…...…18
1-4 主要成果………………….…………...………...……………..19
1-5 論文結構說明………………………………...…...………...…20
第二章 低壓用戶負載曲線之辨別.…………...…………..…………..…21
2-1 簡介……………………….…………...…………..……………..21
2-2 簡單負載曲線之建立..….…………...…………..………………23
2-3 用戶負載曲線集群分析-模糊c平均法（Fuzzy c-means）…….25
2-4 以類神經網路為基礎之低壓用戶歸類………..…………..……28
2-5 本章結論……..………….…………...…………..………………32
第三章 配電變壓器負載估計...…………..……...………………………34
3-1 簡介……………………….…………...…………..……………..34
3-2 負載調查程序..………….…………...…………..………………35
3-3 變壓器負載模式之建立………...………………….……………38
3-3-1 建立各用戶類別負載曲線之歸屬函數………….………39
3-3-2 配電變壓器負載分配…………………………………….44
3-4 本章結論……..………….…………...…………..………………47
第四章 應用饋線重組改善饋線負載因數……………...……………….49
4-1 簡介……………………….…………...…………..……………..49
4-2 問題描述…………………………………………...…...……..…49
4-3 以粒子群最佳化為基礎之求解程序………...………………….51
4-4 本章結論……..………….…………...…………..………………55
第五章 測試結果……………………………………………...………….56
5-1 用戶集群分析和歸類測試………………………....….…..…57
5-2 配電變壓器負載模型之建立與測試……………………………68
5-2-1 模糊負載潮流之應用……..………………...…………....74
5-3 饋線重組改善負載因數之測試………………...……………….75
5-4 本章結論……..………….…………...…………..………………80
第六章 結論及未來研究方向………..……….………..………...………82
6-1 結論………......………………………………………………..…82
6-2 未來研究方向……………………………………………………83
參考文獻…...………......………………………………………………..…84

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