本文主要應用多重碎形去趨勢波動分析結合高斯混合模型對電力設備之局部放電類型進行識別，並以實際量測訊號驗證之。為建構一套局部放電智慧監測系統，將整體架構分為三個部分：即時檢測系統、通訊連結、遠端監控系統。首先即時檢測系統利用高頻比流器(HFCT)感測電力設備接地線上的電流脈衝訊號，以高速資料擷取卡(DAQ，NI PXI-5105)進行資料擷取並傳輸到高頻寬嵌入式控制器(NI PXIe-8135)，將量測訊號作多重碎形去趨勢波動分析的特徵提取，再根據IEC 61850的通訊協定將特徵參數傳送至遠端監控系統。遠端監控系統則同時從資料庫中讀取訓練資料來建構類別模型，即為高斯混合模型，其中該模型透過K-means++演算法優化初始參數使系統達到更穩定的效果，最後利用貝氏決策理論對即時接收之測試資料進行判讀處理。本研究將所有分析方法及演算法設計於LabVIEW人機介面上進行實測，並以PD校正器(Calibrator)及局部放電實驗驗證本系統之可行性。

In this study, the multifractal detrended fluctuation analysis combined with the Gaussian mixture model is used to identify the partial discharge type of the power equipment, and to verify by the actual measurement signal. In order to construct a partial discharge intelligent monitoring system, the overall structure is divided into three parts: real-time detection system, communication link, and remote monitoring system. First, the real-time detection system uses the high-frequency current transformer to detect the current pulse signal on the ground line of the power equipment, and uses the NI PXI-5105 for data acquisition and transmission to the high-bandwidth embedded controller NI PXIe-8135. Then, feature parameters of the actual measurement signal are extracted by the multifractal detrended fluctuation analysis and is transmitted to the remote monitoring system using the protocol of IEC 61850. The remote monitoring system simultaneously reads the training data from the database to construct the category model, which is the Gaussian mixture model. The model optimizes the initial parameters through the K-means++ algorithm to make the system have more stable effects. Finally, the Bayesian decision theory is used to identify the measurement data received. In this study, all analytical methods and algorithms were designed on the human-machine interface of LabVIEW for measurement, and the feasibility of the system was verified by PD calibrator and partial discharge experiments.

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