根據國外調查指出，電弧事故是引起電器火災的主要原因之一，當低壓線路發生電弧故障時，電弧所產生的高溫可能點燃附近的易燃物，而導致火災的發生。本論文藉由電弧故障檢測平台，針對線路中供應不同特性的家電負載進行實驗，使用快速傅立葉轉換分析線路電流之時域和頻域特性，作為開發電弧故障檢測方法的基礎。針對電弧故障檢測，本文結合離散小波轉換和高頻能量累積法得到線路電流每週期的特徵向量，再分別結合倒傳遞類神經網路與決策樹組合出三種檢測方法。利用這三種方法進行線路正常運轉、串聯電弧故障和發生開關電弧的測試，並與商用電弧故障斷路器(Arc-Fault Circuit Interrupter, AFCI)的檢測結果相比較。由測試結果顯示，所設計的三個方法，誤判情形低於商用AFCI。而決策樹在硬體消耗及檢測準確度均優於倒傳遞類神經網路。最後，以FPGA實現三種檢測方法，驗證本文所提出之電弧故障檢測方法於硬體上實現的可行性。

According to foreign investigations, arc faults are one of the main causes of electrical fires. When an arc fault occurs in a low-voltage system, the high temperature generated by arcing may ignite nearby flammable materials and lead to a fire. In this thesis, the arc fault experiment platform is used to test the power line which feeds the load of household appliances with different characteristics. The fast Fourier transform(FFT) is used to analyze the time domain and frequency domain characteristics of the line current, which are the basis for developing arc fault detection methods. For the arc fault detection, this thesis combines the discrete wavelet transform(DWT) and the accumulation high frequency energy method to obtain the characteristic vectors of the line current per power cycle. These vectors are applied to back propagation neural network(BPNN) and decision tree, repectively. Three detection methods are tested for the line under normal operation, series arc fault, and switching arc. The test results are compared with the commercial arc-fault circuit interrupter (AFCI). From the test results, it is showed that the detection accuracy is higher than the commercial AFCI. The decision tree is superior to the back propagation neural network in hardware consumption and detection accuracy. Finally, three detection methods are implemented on FPGA to verify the feasibility of the arc fault detection method proposed in hardware.

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