太陽能發電近年來逐漸興起，使得直流電力在電力系統的使用越來越廣泛，直流線路的電弧故障也漸漸受到重視。伴隨著電弧的強光與高熱現象容易導致火災事故，國內外皆有太陽能光電系統因直流電弧故障引發火災的案例，因此美國與台灣針對太陽能光電系統制訂電弧故障保護的相關規範。本論文建立一個直流串聯電弧實驗平台，紀錄在直流電阻負載與串接逆變器下，於不同實驗條件發生與未發生串聯電弧故障之電流訊號。首先於MATLAB利用離散小波轉換取得線路電流波形的時域-頻域特徵，再利用高頻能量累積法算出各個測量週期的累積能量，並透過決策樹訓練出一套適合的檢測法，將這套檢測方法實現於FPGA開發板。最後將檢測方法應用至量測之實驗數據，並與商用太陽能直流電弧偵測器進行比較。本論文所提方法能夠正確判別串聯電弧故障發生與否，若未來能將本論文所提出的檢測法與智慧電表或其他檢測裝置做結合，將有助於提升用電安全。

Because photovoltaic systems have gradually emerged in recent years, they make the use of DC power in the power system more and more extensive. DC arc faults on DC lines have gradually become seriously. The arcing accompanied with high thermal energy and spark is easy to cause serious fires. USA and Taiwan have developed regulations that PV system shall include a DC arc fault circuit protection device. In this thesis, it is to establish an experiment platform to collect line current data under normal operation and series arc fault. Experiments include resistive load and inverter operating on different conditions. First of all, it is to use the discrete wavelet transform (DWT) to obtain the time-frequency domain characteristics of line current waveforms. Then the high-frequency components are used to calculate the cumulative energy for each measurement period. It then train a suitable detection method by using decision tree. At last, the detection method is applied to the FPGA preliminarily. The decision tree detecting method are applied to experiment data and the results are compared to commercial PV AFD. In this thesis, the proposed method can correctly recognize normal operation and series arc fault. If the detecting method in this thesis can be combined with a smart meter or other detection devices in the future, it could effectively improve the power safety.

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