根據國外調查指出，電弧事故是導致電氣火災的原因之一，當線路發生電弧故障時，電弧產生的火花與高溫，若未及時排除，可能點燃周遭的易燃物，導致發生火災。為開發電弧故障之精準及快速檢測技術，本論文建立直流串聯電弧及交流串聯電弧實驗平台，分別對直流及交流線路電弧進行實驗。首先實驗數據用離散小波轉換與高頻能量累積值得到特徵向量，再使用倒傳遞類神經網路與K近鄰演算法訓練出兩種檢測法，最後將檢測法實現於FPGA開發版上。由測試結果顯示，本論文設計之檢測法，在線路正常運轉和發生串聯電弧時，均能判斷出正確結果，且準確度優於商用電弧故障偵測器(Arc-Fault Detector,AFD)與商用電弧斷路器(Arc-Fault Circuit Interrupter,AFCI)。而K近鄰演算法的優點有容易理解、容易實現和運算簡單，在硬體消耗上較倒傳遞類神經網路要少，若未來使用硬體實現，此法將可節省硬體資源。

According to foreign investigations, arc faults are one of the causes of electrical fires. When an arc fault occurs on electric wire, sparks and high temperatures may ignite surrounding flammable materials and cause fire if arcs are not eliminated in time. 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 loads feeders respectively. First of all, the experimental data is used to obtain the eigen vector by discrete wavelet transform and accumulation high frequency energy method. Thereafter, the back propagation neural network(BPNN) and K nearest neighbor(KNN) algorithm are used to train two detection methods. At last, these detection method are implemented on the Field Programmable Gate Array(FPGA).From the test results, it is showed that the detection method designed in this thesis can detect correctly when electric wires are under normal operation and series arc fault. And the accuracy of our method are better than the commercial arc fault detector(AFD) and the arc fault circuit interrupter(AFCI). The advantages of the K-nearest neighbor algorithm are easy to understand, easy to implement, simple to operate and has less hardware consumption than that of the back propagation neural networks. If the K-nearest neighbor algorithm is implemented in the future, it will save hardware resources.

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