電力電纜劣化的原因包括廠內製造瑕疵、現場施工瑕疵、或老化等，若是放電能量超過該導線之絕緣材料之耐受程度時，電力電纜絕緣將會崩潰，而造成局部放電，一旦局部放電發生，將危害電力電纜而造成重大損害。本文主要是利用高頻感測器(HFCT)量測單芯XLPE電力電纜局部放電訊號，將瑕疵的電氣訊號其原始波形，使用小波濾雜訊法(Wavelet de-noising)濾除環境雜訊而萃取其脈衝特徵值繪製成圖譜後，蒐集不同種瑕疵的資料，分別使用分群演算法(k-means)及倒傳遞類神經網路兩種方法來辨別不同種瑕疵之間的關係，瑕疵實驗主要是針對常見的電纜瑕疵進行模擬分析與辨識，驗證本論文所提出的方法與實驗結果的一致性。

In this thesis, we aim to develop a system to recognize partial discharge (PD) signal patterns in XLPE power cables. The PD signals are detected by high-frequency current transformer (HFCT) sensors and the PD patterns can be extracted from the raw data with wavelet de-noising method. To identify the PD patterns, the K-means algorithm is presented to distinguish different kinds of faults in power cables. Moreover, the features of 3D patterns extracted from PD patterns can be identified by back propagation neural networks (BPN). On the basis of these results, the system can provide inspection personnel a powerful tool to determine possible PD fault types and maintain related equipment.

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