依據國外調查指出，電弧事故為發生家庭電氣火災事件之主要原因之一，應要小心應對。無論在交流或直流電力系統中，當線路發生電弧故障時，若未能及時將故障排除，電弧產生的火花與高溫可能會對電路設備造成損害，甚至可能點燃附近的易燃物，引起火災。為設計出精準且迅速的電弧故障檢測技術，本論文建立交流串聯電弧與直流串聯電弧實驗平台，分別對於交流與直流線路進行電弧故障的實驗。首先使用高頻能量累積值、快速傅立葉轉換與帶通濾波器訊號處理技術，先得到電流特徵向量。再使用Python的軟體系統撰寫機率類神經網路(PNN)與Keras類神經網路，兩種檢測法，並與商用電弧斷路器(AFCI)與電弧故障偵測器(AFD)進行比較。經由測試後發現，當線路在正常運轉與發生電弧故障時，三種訊號處理方法結合兩種類神經檢測法皆具有正確的判斷結果。因PNN檢測法只需調整一種模組參數，使網路模型能在少量訓練次數下，達到省時且精準的檢測結果。

According to some foreign investigations, arc faults are one of the main reasons of home electrical fires. They should be handled carefully. When an arc fault occurs on electric wire, sparks and high temperatures may cause damage to the electrical equipment. They even might ignite surrounding flammable materials and cause a fire if arcs are not eliminated in time. In order to design accurate and fast detection technology of arc faults, this thesis establishes a DC series and AC series arc fault experiment platform to conduct experiments on DC and AC load feeders, respectively. First of all, the experiment data is used to obtain the eigenvectors by the accumulation high frequency energy method, the fast Fourier transformation, and the bandpass filter. Thereafter, the probabilistic neural network (PNN) and the Keras neural network are two detection methods that are developed in Python. we compare these methods to the commercial arc fault circuit interrupters (AFCI) and the arc fault detectors (AFD) in detection. After test, it can be seen that two proposed methods combined with three signal processing methods can correctly judge results when electric wires are under normal operation and series arc fault. We only need to adjust one module parameter in the PNN detection method. Therefore, the network model can achieve timesaving in network training and accurate detection results.

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