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計算非正弦波視在功率中包含的電壓失真功率、電流失真功率及諧波視在功率，並且分析線路電流之時域及頻域特性。經過比較後，電流失真功率較適合作為判斷電弧發生之特徵向量。本文運用離散小波轉換結合高頻能量及電流失真功率，組合出四種特徵向量。利用這四種特徵向量於決策樹檢測法測試，最後再將測試結果錯誤率最低的特徵向量與倒傳遞類神經檢測法進行比較。由測試結果顯示，決策樹檢測法錯誤率最低。因此，以FPGA實現決策樹檢測方法，進行線路正常運轉、串聯電弧故障及發生開關電弧的測試，並與商用電弧故障斷路器的檢測結果相比，驗證本文所提出的電弧故障檢測方法之可行性。

In this thesis, the arc fault experiment platform is used to conduct experiments on household appliances with different characteristics in the line. The fast Fourier transform (FFT) is used to calculate the voltage distortion power, current distortion power and harmonic apparent power, which are composed of non-sinusoidal apparent power components. They are compared to reveal and analyze the time domain and frequency domain characteristics of line current waveforms. It is shown that the current distortion power is more suitable as the characteristic vector for judging the arc faults. The discrete wavelet transform (DWT) is also used in this thesis to obtain high frequency component energy, which combines current distortion power to produce four characteristic vectors. Four characteristic vectors are tested by using the decision tree. Finally, the characteristic vector with the lowest error rate in the test results is compared with the method of the back propagation neural network. From the test results, the decision tree detection method has the lowest error rate. Therefore, detection tree method is implement on FPGA. It is tested for the line under normal operation, series arc fault, and switching arc. Compared with the arc-fault circuit interrupter (AFCI), the test results verify the feasibility of the arc fault detection method proposed in this thesis.

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