本論文應用類神經網路，推導配電饋線線路損失分析模式。為提升損失分析之效能，利用自動圖資及設備管理系統(Automated Mapping and Facility Management system, AM/FM)資料庫中完整之配電設備資料，透過拓樸分析之追蹤及節點減量技巧，建立簡化之配電饋線電網架構。另以負載特性調查為基礎，根據抽樣用戶之用電資料庫，整理住宅、商業及工業類型用戶用電資料，以統計分析推導各類型用戶標準化日負載模式。配合用戶服務資訊系統(Customer Information System, CIS)之用戶售電資料，依據標準化日負載模型，並利用AM/FM資料庫所提供之線路變壓器與用戶電號的對應關係，推估線路變壓器每小時負載量。將簡易之饋線電網模型結合饋線相關資料，利用三相負載潮流分析程式，計算饋線損失，並建立類神經網路訓練資料。同時應用類神經網路訓練之方法，學習配電饋線出口供電量、線路長度、變壓器容量、饋線供電電壓與饋線損失之對應關係，估算不同類型饋線之損失。
根據饋線損失計算可知都會區之饋線由於線路較短但用戶數眾多，且線路變壓器負載較重，故變壓器鐵損及低壓線損較高；反之，在郊區之饋線大多為長距離之架空線，供給用戶分散較廣，其線路損失會較高。對於台電系統而言，透過本論文所提出配電饋線損失分析模式，能夠迅速估算配電饋線之損失，以提供各區處改善配電系統運轉效率之參考。

This thesis is to study the distribution system loss by applying artificial neural networks(ANN). To enhance the efficiency of loss analysis, the distribution system network has been obtained by retrieving that component information for the automated mapping and facility management system (AM/FM). The topology process and node reduction has also been applied to identify the network configuration and the input data for load flow analysis. The load survey study is used to derive the typical load patterns of various customer losses. The monthly energy consumption of customers by each transformer, which has been retrieved for the customer information system(CIS), is used to derive the hourly loading of each distribution transformer. The three phase load flow analysis has been performed for different types of distribution feeders to solve feeder loss to generate the data set for the training and testing of neural networks. The ANN for distribution loss analysis, which has been obtained after network training, can solve the distribution system loss very efficiently according to the feeder load demand, length, transformer capacity and voltage level.
With short feeder length and voluminous customers served by the distribution feeders in urban area, the transformer core loss and secondary line loss contribute most of the distribution feeder loss. On the other hand, the line loss of rural distribution feeder is more significant because of the longer distribution lines to serve more scattering customers. With the neural based distribution system loss modeling, the distribution system loss can be estimated very easily, which can provide Taipower a good reference to enhance the operation efficiency of distribution system.

目錄
中文摘要 Ⅰ
Abstract Ⅱ
目錄 Ⅳ
圖目錄 Ⅵ
表目錄 Ⅷ
第一章 緒論 .......................................1
1-1 研究背景及目的 ......................................1
1-2 研究步驟 ............................................2
1-3 章節概要 ............................................5
第二章 自動圖資及設備管理系統...........................7
2-1 前言.................................................7
2-2 圖資運轉系統.........................................8
2-3 自動圖資及設備管理系統之操作........................10
2-4 拓樸分析............................................13
2-5 節點減量............................................20
第三章 負載特性調查與用戶服務資訊系統..................23
3-1 前言................................................23
3-2 負載調查與負載組成..................................24
3-3 用戶服務資訊系統....................................28
3-4 饋線別負載組成......................................31
3-4-1 饋線MC71區段Q1208FD04負載組成.....................33
3-4-2 饋線LY43 區段AB53ED50負載組成.....................38
第四章 饋線損失模型與類神經網路介紹....................43
4-1 前言................................................43
4-2 饋線損失模型介紹....................................44
4-2-1 線路模型..........................................44
4-2-2 變壓器模型........................................47
4-2-3 三相潮流分析......................................51
4-3 類神經網路介紹......................................55
4-3-1 類神經網路之主要架構..............................55
4-3-2 類神經網路學習法..................................57
第五章 配電饋線損失分析................................63
5-1 前言................................................63
5-2 配電饋線損失........................................64
5-3 應用類神經網路於配電饋線損失........................70
5-4 測試饋線之類神經網路輸出............................72
5-5 其他類型之神經網路演算法收斂比較....................74
5-6 回歸分析與類神經網路之比較..........................75
第六章 結論及未來發展..................................77
6-1 結論................................................77
6-2 未來發展............................................79
參考文獻................................................80

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