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Aiming at evaluating the state of the photovoltaic (PV) array and improving the reliability of the PV system, a fault diagnosis method for PV arrays based on fault parameters identification is proposed in this paper. Compared with existing fault diagnosis methods, the proposed method has advantages of recognizing concurrent faults and describing each fault quantitatively by identifying fault parameters from the measured current-voltage (I–V) curve of the PV array. The methodology consists of three parts. Firstly, functional relationships between unknown parameters in the one-diode model of PV cells with environmental parameters are obtained by parameters extraction. Secondly, a code-based fast fault simulation model (CFFSM) is established to simulate I–V curves of the PV array under various faulted conditions. Thirdly, by determining the fault parameters to be identified and constructing an objective function that is the error between the simulated I–V curve with the measured I–V curve, an optimization problem is formulated, in which optimal fault parameters are identified by applying the differential evolution (DE) algorithm. The validation experiments in summer and early spring show that the proposed diagnosis method can identify the parameters of up to three concurrent faults, including partial shading, short circuit, and increased series-resistance losses, under good irradiance condition with high accuracy.

Aiming at monitoring photovoltaic (PV) systems, evaluating the degree of fault and locating the fault automatically under different outdoor conditions, this paper discusses a new procedure of fault detection and evaluation of fault degree of the PV system. For the PV array connected by PV modules in series and parallel, each string shares the same voltage. The value of current can be used to identify the underperformed strings. In addition, considering the non-stationary stochastic characteristics of current of PV strings, the local outlier factor (LOF) is applied to detect the fault in PV system by evaluating the deviation between the observed data and the whole data. Nevertheless, the LOF method is more suitable for large samples and the LOF value varies with the value of string current. Hence, the conventional LOF method is not suitable for evaluating the fault degree. In order to apply this method to different scale PV systems to detect the fault accurately and evaluate the fault degree, a modified algorithm is proposed in this study. The simulations and experiments based on the model of PV array in MATLAB/Simulink and the 10 kWp PV power plant built on the campus of Hohai University are implemented. The results of experiments reveal that the modified LOF has good performance in fault detection and fault degree evaluation in different scales of the PV systems.