近年來由於電力電子設備廣泛的應用，電力諧波(Power Harmonics)造成之影響日漸嚴重，由於非線性負載的特性造成大量諧波注入電力系統，嚴重諧波污染影響電力品質，包括不斷電系統、整流器、變頻器等等之非線性負載在現代化工廠中已是不可或缺之設備，但其衍生之電力諧波問題，卻使得設備誤動作，電力電容器燒毀，造成工廠之重大損失等問題。

為了確保電力系統的電力品質，在電力系統中找出諧波源(Harmonic Sources)是一項重要的課題，本文提出以類神經網路(Artificial Neural Network)應用於電力系統諧波源與位置偵測。將根據負載諧波源的特性，透過諧波潮流分析程式，分析各種負載產生不同頻率之諧波成份，依據各種頻譜圖樣作為訓練資料，期望能執行偵測諧波源之工作。提出以機率神經網路(Probabilistic Neural Networks)與小波網路（Wavelet Probabilistic Network）應用於諧波源位置偵測(Harmonic Source Location)。根據負載諧波源的特性，透過諧波潮流分析程式，分析各種負載產生不同頻率之諧波成份，依據各種頻譜圖樣作為訓練資料，期望能執行偵測諧波源之工作。

本文提出的諧波源偵測系統，以IEEE-14 bus系統在個人電腦上模擬測試驗證其可行性。

The technology of power electronics is used increasingly during recent years, and the electronic power facilities are used more and more in the power system. The non-linear electronic loads produce heavy harmonic currents and could significantly degrade the power quality. Nonlinear loads, including the un-interruptible power supply, motor control and converter, etc, are important equipment in a modern factory, however, these nonlinear loads could lead to power facility malfunction and capacitor damage. The harmonics would eventually cause severe unexpected capital loss.

Identification of harmonic sources location becomes an important study for power quality. An effective tool is thus helpful for the harmonic source locating. This paper proposes a method to deal with the harmonic sources and location detection in the power system by using the artificial neural network (ANN). The non-linear loading characteristics are studied by the power flow analysis, and then the proposed methodology uses the Probabilistic Neural Networks（PNN）and wavelet-probabilistic network (WPN) for harmonic source locating.

An IEEE 14-bus power system is used for study to show the effectiveness of the proposed approach.

目 錄
摘要 I
Abstract III
目錄 V
圖目錄 VIII
表目錄 X

第一章 緒論 1

1.1 研究動機 1
1.2 研究背景及方法 1
1.3 論文內容概述 3

第二章 諧波源偵測之研究背景及問題描述 5

2.1諧波之定義 5
2.1.1前言 5
2.1.2傅立葉級數及電力諧波之基本定義 5
2.1.3諧波失真率之定義 7
2.2 諧波產生之原因 9
2.3 諧波之影響 10
2.4諧波管制之標準 15
2.4.1諧波源需管制評估之對象 15
2.4.2諧波管制標準 16
2.4.3台灣電力公司諧波管制標準 18
2.5電力系統諧波潮流分析模型 19
2.5.1元件模型 19
2.5.2諧波負載潮流 25

第三章 研究方法介紹 28

3.1前言 28
3.2類神經網路介紹 28
3.2.1類神經網路模型 29
3.2.2人工神經元模型 32
3.2.3常用的非線性轉換函數 33
3.2.4類神經網路的組成 35
3.3類神經網路的種類與特性 36
3.3.1以學習策略分類 36
3.3.2以網路架構分類 37
3.3.3類神經網路的選用 38
3.3.4類神經網路的特性 39
3.4小波理論 40
3.4.1 Fourier Analysis 簡介 40
3.4.2短時傅氏轉換—STFT簡介 41
3.5小波(Wavelet)特性 43
3.6小波轉換種類 45

第四章 類神經網路於諧波源偵測架構與人機介面 47

4.1前言 47
4.2機率類神經網路 48
4.2.1貝氏分類器 48
4.2.2機率神經網路演算法 51
4.3小波網路 52
4.3.1簡介 52
4.3.2小波轉換 54
4.3.3小波網路演算法 55
4.4諧波源位置偵測系統之設計 57
4.5訓練資料之建立 62
4.6諧波源偵測系統 65
4.7結合小波分析之諧波源偵測系統設計 66
4.8人機介面之建構與偵測架構之結合 71

第五章 系統實例整合測試與討論 73

5.1系統簡介 73
5.2測試模擬__PNN機率神經網路之諧波源位置偵測 74
5.3測試模擬__量測儀表之擺置位置 80
5.4測試模擬__小波網路之諧波源位置偵測 85
5.5 PNN機率神經網路與小波網路偵測架構之比較分析 88
5.6平滑參數 值之決定與測試 90
5.7本章結論 91

第六章 結論與未來的研究方向 92

6.1 結論 92
6.2 未來研究方向 93


參考文獻 95

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