An Intelligent Fault Diagnosis Approach for PV Array Based on SA-RBF Kernel Extreme Learning Machine
Yue Wu, Zhicong Chen, Lijun Wu, Peijie Lin, Shuying Cheng, Pei-Min Lu · Energy Procedia · 2017
In this paper, based on an improved radial basis function (RBF) kernel extreme learning machine (ELM) optimized by simulated annealing algorithm, a novel intelligent fault diagnosis approach for photovoltaic (PV) array is proposed. Firstly, three common PV array faults are analyzed in detailed. And then, the ELM is proposed to automatically detect the faults of PV array. Moreover, simulated annealing (SA) algorithm is exploited to optimize the parameters of RBF-ELM model. Finally, a simulation experiment is carried out to verify the proposed SA-RBF-ELM and the result shows that the proposed SA-RBF-ELM approach can quickly and accurately identify the typical PV faults including short circuit, aging and partial shadow.