本論文提出電力設備局部放電(Partial discharge)源量測與定位之研究，電力設備包含環氧樹脂絕緣礙子(Epoxy resin insulator)、電力電纜(Power cable)與氣體絕緣開關(Gas insulated load break switches)。首先是針對環氧樹脂絕緣礙子老化特性加以探討。再者，在電力電纜上分別採用脈衝電流檢測與音射法，在局部放電譜圖(Phase resolved partial discharge)上確認局部放電的發生。進而，本論文提出應用機率神經網路(Probabilistic neural networks)與模糊C均值分群法(Fuzzy c-means clustering approach)等之專家系統來定位局部放電發生的位置。本論文在氣體絕緣開關、電力電纜與其接合處設計三處的人工瑕疪做為模擬可能發生局部放電的位置。其中離散小波轉換(Discrete wavelet transform)是被應用於在從高頻比流器(High frequency current transformer)所量測信號之雜訊的抑制；白色高斯雜訊(White Gaussian noise)則是被使用加入局部放電信號中，用來模擬為高雜訊的量測環境。本論文所提供的方法，希望能夠幫助電機工程師作出準確的判斷。為了準確的發現不同瑕疪位置，本論文所提供的方法是從高頻比流器測得的信號，經過離散小波轉換來濾除雜訊，再利用特徵提取與統計分析。最後，從實驗結果證明，本論文所提供的方法能夠有效的準確在實際局部放電測量局部放電源的位置。

This dissertation presents the study of measurement and allocation of partial discharge (PD) sources in power equipment. The epoxy resin insulator, power cable and gas insulated load break switches (GILBS) are included in power equipment. First of all the epoxy resin insulator is discussed performance characteristics of aging. Next proposes an approach to show patterns of signal that detected by using electric and acoustic emission (AE) methods can be used for the identification of partial discharge (PD) events in power cable. Therefore, this dissertation proposes an approach for location of partial discharge sources in the power cable and gas insulated load break switches using probabilistic neural networks (PNN) and the fuzzy c-means clustering approach (FCM). Three different defect positions are designed in the power cable and gas insulated load break switches. The three different defect positions of partial discharge occurrence are located by the proposed method. Discrete wavelet transform (DWT) is employed to suppress noises of measured signals by the high-frequency current transformer (HFCT). The white Gaussian noise, which simulates the high noise environment, are added to the partial discharge signals, when making measurements. The proposed method can assist electrical engineers to make accurate statistical judgments. To accurately discover the different defect positions, the proposed method uses feature extraction and statistical analysis of the measured signals. Lastly, experimental results validate that the proposed approach can effectively determine the location of partial discharge sources in practical partial discharge measurement.

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