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In the DC power system, arc fault caused by insulation defects and loose joints is easy to cause fire, explosion, and other safety accidents, but it is not easy to extinguish and detect. Therefore, it is of great significance to study the electrical characteristics of the DC arc and propose effective detection methods. Detection of DC arc faults in photovoltaic systems has attracted widespread attention from scholars at home and abroad. In this paper, several experimental conditions that may affect arc characteristics are analyzed. By using the photovoltaic strings and the arc generator, a lot of field experiments are carried out to obtain the current data during arc fault in an actual photovoltaic system under different conditions of current, cable lengths, and arc gaps. According to the experimental data under different conditions, several characteristic variables from both the time domain and the frequency domain are extracted to identify the arc fault. In this paper, three machine learning methods in the field of artificial intelligence: Random Forest, Support Vector Machine, and Bayes Classifier, are used to learn the experimental samples. Through training, a model is obtained in each method to distinguish between normal current and arc fault current. Then, the three classifier models are validated by hundreds of sets of experimental data under normal and arc conditions, and the classifier with the highest accuracy is found. According to the results, the proposed method can accurately identify arc fault conditions and normal working conditions. Among them, the random forest algorithm has the highest recognition accuracy.

An energy balance algorithm is desirable for the DC-link voltage control of Grid tied Battery connected bidirectional Photovoltaic (PV) inverter system. The system must satisfy the requirements of Battery and the DC-bus voltage requirements. This paper proposes an energy balance based algorithm to stabilize the voltage across the DC-link capacitor which will automatically stabilizes the DC-bus voltage. The proposed energy balance algorithm generates the reference Grid current for current control algorithm which controls the inverter to inject sine wave current into the Grid. Thus, the proposed algorithms accordingly balance the voltage across the DC-link capacitor and controls the inverter to inject sine wave current into the Grid. The system shows an excellent performance based on the proposed algorithms which includes tracking accuracy, tracking speed, settling time and overshoot, which can be seen in the simulation results.