Local outlier factor-based fault detection and evaluation of photovoltaic system

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.