依據國外調查指出，當低壓線路發生電弧故障時，若未及時將故障清除，則電弧產生的高溫與火花可能會對電器設備造成損害，甚至燃起周遭的易燃物，導致發生火災。因此電弧故障為家庭電氣火災事件之主要原因之一，需慎重看待。本論文建立低壓交流串聯電弧與直流串聯電弧實驗平台，針對線路中供應不同特性的家電負載進行串聯電弧故障實驗。首先使用小波轉換、快速傅立葉轉換與帶通濾波器訊號處理技術，先取得線路電流特徵向量。再使用Python軟體撰寫貝氏分類器與機率類神經(probabilistic neural network,PNN)兩種檢測法作為串聯電弧故障檢測，並與商用電弧斷路器(arc fault circuit interrupter,AFCI)與商用電弧故障偵測器(arc fault detector,AFD)進行比較。由測試結果顯示，當線路在正常運轉與發生串聯電弧故障時，在線路供應於某些負載且發生串聯電弧故障時，貝氏分類器有出現誤判的情形，但其仍為廣泛使用的分類器之一，訓練速度快，達到省時且精準的檢測結果。

According to foreign research, when an arc fault occurs on low voltage circuit and is not cleared immediately, the high temperature and sparks generated by the arc may cause damage to electrical equipment and even ignite flammable materials around it, leading to a fire. Therefore, arc faults are one of the main reasons for household electrical fires and should be prevented. This thesis establishes an experimental platform for AC and DC series arc and conducts arc fault experiments on household electrical loads with different characteristics supplied on low voltage circuit. First, signal processing techniques such as wavelet transform, fast fourier transform, and band pass filters are used to obtain the eigenvector of the circuit current. Then, the Bayesian classifier and the probabilistic neural network (PNN) are two detection methods written in Python software, compared with commercial arc-fault circuit interrupter (AFCI) and arc-fault detector (AFD). The test results show when the circuit operating normally or occuring series arc faults on certain loads are supplied, the Bayesian classifier may be misjudgments, but it is one of the widely used classifiers with fast training speed and accurate detection results, achieving time-saving and precise detection.

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