Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents - Chen, Zhicong; Han, Fuchang; Wu, Lijun; Yu, Jinling; Cheng, Shuying; Lin, Peijie; Chen, Huihuang (Energy conversion and management, 2018)
•A novel random forest based fault diagnosis method is proposed for PV arrays.•Fault features only consist of PV array voltage and string currents.•Gird search method is used to optimize the parameters of random forest algorithm.•Both simulation and real experiments validate the high accuracy and generalization performance.•The proposed fault diagnosis method is implemented in a developed system prototype. With the rapid growth of installed capacity of photovoltaic power systems, status monitoring and fault diagnosis of PV arrays becomes increasingly important for improving the energy conversion and maintenance efficiency. In recent years, many machine learning algorithms were successfully applied to automatically build fault diagnosis models using the fault data samples. However, most of them suffer overfitting problem and the generalization performance is still limited. In this paper, the random forest (RF) ensemble learning algorithm is explored for the detection and diagnosis of PV arrays early faults (including line-line faults, degradation, open circuit, and partial shading), which combines multiple learning algorithms to achieve a superior diagnostic performance. The proposed RF based fault diagnosis model only takes the real-time operating voltage and string currents of the PV arrays as the fault features, which is irrelevant to the environment conditions. In addition, a grid-search method is used to optimize the parameters of the RF model by minimizing the out-of-bag error estimation, so as to improve the fault diagnosis model. In order to obtain sufficient fault data samples, comprehensive fault experiments are conducted on both a Simulink based simulated PV system and a laboratory PV system. The simulation and experimental results both demonstrate that the optimized RF based fault diagnosis model can achieve a high overall detection and diagnosis performance. Moreover, the comparison results indicate that the generalization performance of the proposed RF based model is better than the one of the decision tree based model. Therefore, the proposed optimal RF based method is an effective and efficient alternative to detect and classify the faults of PV arrays. Furthermore, the proposed RF based fault diagnosis model is successfully integrated into a Matlab based real-time monitoring system prototype developed for the laboratory PV system, which validates the practicability as well.