近年再生能源的興起，使得直流電力在電力系統的比例逐漸增加，對於直流電弧故障也日漸重視。電弧所產生的火花與高溫容易導致火災，在國外已有太陽能光電系統因直流電弧故障引發火災的事故，因此美國與台灣針對太陽光電系統制訂電弧故障電路保護的相關規範。本論文建立直流電弧故障實驗平台，先將實驗數據利用快速傅立葉轉換(Fast Fourier Transform, FFT)進行信號處理，並擷取電流特徵。再分別應用模糊系統與學習向量量化設計串聯電弧故障檢測方法，最後將串聯電弧故障檢測系統初步實現於現場可程式編輯閘陣列(Field Programmable Gate Array, FPGA)開發板，並與商用太陽能直流電弧偵測器(Photovoltaic Arc Fault Detector, PV AFD)進行比較。由實驗結果得知，兩種檢測方法皆能夠正確判斷正常運轉與串聯電弧故障，並在線路增長的情況下，誤判情形比商用PV AFD還要少。

The renewable energy is rising in recent years, and so the proportion of DC power in power system is gradually increased. DC arc fault protection is also becoming more important in system. The arcing will accompany with high temperature and sparking that is easy to cause serious fires. Several oversea examples of fires caused by arcing fault at PV system were reported. USA and Taiwan have announced regulations that PV systems shall include DC arc fault circuit protection devices. In this thesis, it is to establishe an experiment platform of DC arc fault. First of all, the experimental data are analyzed by the Fast Fourier Transforms (FFT) for the signal processing to capture characteristics of current. Thereafter, these extracted characteristics are applied to the fuzzy system and the learning vector quantization (LVQ), respectively. Therefore, the two detecting methods are designed. At last, two detecting methods are implemented by using the Field Programmable Gate Array (FPGA) preliminarily. The detecting results are compared to commercial photovoltaic arc fault detection (PV AFD). According to the experiment results, these detecting methods can correctly and effectively detect series arc fault. And in the case of power circuit growth, they have better ability than commercial AFDs.

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