當線路發生電弧故障時，若未能及時做適當的處理，電弧所產生的高溫火花可能會對於配電系統、輸電系統以及電路設備造成損害，甚至可能點燃附近的易燃物，造成火災。且根據國外調查指出，電弧事故是導致電氣火災的主因之一。為探討出更快速且精準的電弧故障檢測技術，本論文建立直流串聯電弧及交流串聯電弧實驗平台，分別對於直流與交流線路進行電弧故障的實驗。首先使用離散小波轉換、快速傅立葉轉換、電流失真功率與濾波器訊號處理方式得到特徵向量。再使用倒傳遞類神經網路與邏輯斯迴歸兩種檢測法，來檢測交流電路與直流電路之電弧故障檢測。兩種檢測法結合四種訊號處理方法，從結果觀察出，訊號處理方法對於串聯電弧檢測有很大影響。設計出之檢測法，雖然在某些線路供應負載且發生串聯電弧故障時，可能出現誤判的情形，但邏輯斯迴歸的優點是有易於理解、容易實現，且運算簡單。

When an arc fault occurs on electric wire, sparks and high temperatures may cause damage to distribution systems, transmission systems and electrical equipment. It even would ignite surrounding flammable materials and cause a fire event if arcs are not eliminated in time. According to some foreign investigations, arc faults are one of the reasons of electric fires. In order to develop accurate and fast detection technology, this thesis establishes a DC series arc and AC series arc experimental platform to conduct experiments on DC and AC load feeders respectively. First of all, the experimental data is used to obtain the eigenvectors by the discrete wavelet transform, the fast Fourier transformation, the current distortion power, and the waveform filter. Thereafter, the back propagation neural network (BPNN) and the logistic regression algorithm are two methods to detect series arc fault on AC circuits and DC circuits. Two detection methods will be combined with four signal processing methods. The signal processing has a great influence on series arc fault detection method. Although the detection method designed in this thesis may cause misjudgments when a series arc fault occurs in line, but the advantages of logistic regression are conducive to understanding, easy to implement, and simple to calculation.

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