An Intelligent Fault Diagnosis Approach for PV Array Based on SA-RBF Kernel Extreme Learning Machine. (May 2017)

• Record Type: Journal Article
• Authors: Wu, Yue; Chen, Zhicong; Wu, Lijun; Lin, Peijie; Cheng, Shuying; Lu, Peimin
• Is Part Of: Energy procedia. Volume 105(2017)
• Page Start: 1070
• Page End: 1076
• Publication Date: 2017-05
• Subjects: PV array -- Fault diagnosis -- Model parameters -- Kernel extreme learning machine -- Simulated annealing; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.03.462
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: 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.