Abstract
It is crucial to detect fault arrays timely to ensure the safe and economic operation of large-scale photovoltaic (PV) power plants. The inconsistencies among different arrays and the random fluctuations of PV output result in uncertainties of the PV array output, and the deterministic fault diagnosis methods decrease the accuracy of PV fault diagnosis methods. In this paper, a probability modeling approach for a PV array electrical parameter distribution, which can effectively solve the problems of the nonlinearity and uncertainty of the PV array output interval, is proposed. Actual PV plant data are utilized to calculate fault indicators and analyze the uncertainty of fault indicator distributions. The t-location scale distribution function is used to fit the fault indicator distributions and to establish a probability model of PV fault indicators in different irradiance ranges. Finally, fault indicator thresholds with different degrees of confidence are obtained to complete the PV array fault diagnosis. The setting of the indicator threshold for PV arrays is based on measured data rather than experience, and it can effectively detect different types of faults. The effectiveness of the proposed method is verified in a real PV plant.
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