臺灣博碩士論文加值系統

電力設備中之絕緣材料因長期運轉、環境變化及人為等因素，將導致絕緣劣化的發生，進而引起無預警的停電事故，甚至造成經濟上的損失，因此如何達到預防性的故障診斷，實為一重要之課題。
本研究旨在基於局部放電的25kV XLPE級電力電纜接續匣瑕疵特徵辨識系統之研究。首先對於電力電纜接續匣常發生故障之可能模式進行整理分析，係由廠商製作各類型人工瑕疵模型的電力電纜接續匣，接著，建置相關的局部放電檢測設備，利用磁場耦合與音射檢測技術針對四種電力電纜接續匣試驗模型進行局部放電量測，將所量測到的試驗數據，經由局部放電3D圖譜與希爾伯特-黃轉換進行分析。其次在運用分形理論擷取圖譜代表性特徵，作為類神經網路之輸入數據，並進行瑕疵模型之辨識。但由於現場加壓檢測時，有時會受到雜訊的干擾，而影響辨識系統之正確性，於是在本文中以人工方式，加入不同大小之隨機雜訊，並探討對於辨識系統之影響。最後，提出以經驗模式分解方法將訊號裡的雜訊濾除，經由實驗數據發現，經驗模式分解確實可有效降低雜訊對訊號的影響，進而提高系統之辨識率。

The insulation materials in power equipment can deteriorate because of long-term operation, environmental changes or human factors, and cause sudden power outages with subsequent economic losses. Therefore, it is important to have the technology to diagnose faults and so prevent such system failures.
In this research we established the characteristics of a defect recognition system based on the partial discharge of a 25KV XLPE power cable joint. First, we analyzed the fault types in power cable joints and made artificial defect models of each common fault type. Then, we constructed partial discharge detection equipment using magnetic coupling and the acoustic emission detection technique for partial discharge detection of the four power cable joint experimental models and analyzed these data by partial discharge 3D images and the Hilbert-Huang transform. Secondly, we used fractal theory to extract representative features of the 3D images as neural network input data and executed defect recognition. However, noise interference can affect recognition accuracy during high-voltage testing; therefore, we artificially put random noise into the original signal to investigate its effect on the recognition system. Finally we have proposed an empirical mode decomposition method to filter out noise and reduce the noise effect in order to increase accuracy.

中文摘要..................................................I
Abstract ....................................................II
致謝 ....................................................III
目錄 ....................................................IV
圖目錄 ....................................................VII
表目錄 ....................................................XI
第一章　緒論 ............................................1
1.1 研究背景與動機.......................................1
1.2 研究目的與方法.......................................2
1.3 章節概要.............................................3
第二章　局部放電與檢測方法簡介...............................5
2.1 局部放電定義與相關名詞...............................5
2.1.1 局部放電定義.........................................5
2.1.2 局部放電相關名詞.....................................5
2.2 局部放電原理與類型 ...................................8
2.2.1 局部放電原理.........................................8
2.2.2 局部放電類型.........................................9
2.3 局部放電訊號檢測方法.................................12
2.3.1 電氣的檢測方法.......................................12
2.3.2 非電氣的檢測方法.....................................16
2.4 局部放電基本圖譜介紹.................................17
2.5 本章結論.............................................20
第三章　訊號轉換分析基本理論.................................22
3.1 小波轉換.............................................22
3.1.1 連續小波轉換.........................................22
3.1.2 離散小波轉換.........................................25
3.1.3 基於小波轉換的雜訊濾除方式...........................27
3.2 希爾伯特-黃轉換......................................29
3.2.1 經驗模式分解.........................................29
3.2.2 希爾伯特頻譜.........................................32
3.2.3 基於經驗模式分解的雜訊濾除方式.......................35
3.3 圖譜比較結果.........................................35
3.4 本章結論.............................................38
第四章　局部放電檢測系統與圖譜分析...........................40
4.1 試驗模型.............................................40
4.2 試驗環境與設備.......................................42
4.3 局部放電檢測器.......................................45
4.3.1 磁場耦合感測器.......................................45
4.3.2 音射感測器...........................................45
4.3.3 量測人機介面.........................................47
4.4 局部放電基本圖譜.....................................49
4.5 本章結論.............................................54
第五章　局部放電特徵擷取與辨識方法...........................55
5.1 分形幾何簡介.........................................55
5.2 特徵擷取方法.........................................60
5.2.1 差盒維數.............................................60
5.2.2 間隙度...............................................61
5.3 類神經網路簡介.......................................63
5.4 類神經網路的架構.....................................64
5.4.1 類神經網路基本架構 ...................................64
5.4.2 倒傳遞網路...........................................69
5.5 本章結論.............................................70
第六章　應用電氣與音射法於瑕疵形態辨識探討...................71
6.1 試驗數據的特徵擷取 ...................................71
6.2 辨識結果與討論.......................................75
6.2.1 倒傳遞網路之辨識結果.................................75
6.2.2 加入雜訊之辨識結果 ...................................77
6.3 本章結論 ............................................81
第七章　結論及未來展望.......................................82
7.1 結論.................................................82
7.2 未來展望.............................................83
參考文獻 .....................................................84
附錄A........................................................89
作者簡述 .....................................................92

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