A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network

Zhenghai Liao, Dazheng Wang, Liangliang Tang, Jinli Ren and Zhuming Liu
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Zhenghai Liao: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
Dazheng Wang: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
Liangliang Tang: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Jinli Ren: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
Zhuming Liu: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China

Energies, 2017, vol. 10, issue 2, 1-11

Abstract: This paper proposes a heuristic triple layered particle swarm optimization–back-propagation (PSO-BP) neural network method for improving the convergence and prediction accuracy of the fault diagnosis system of the photovoltaic (PV) array. The parameters, open-circuit voltage (V oc ), short-circuit current (I sc ), maximum power (P m ) and voltage at maximum power point (V m ) are extracted from the output curve of the PV array as identification parameters for the fault diagnosis system. This study compares performances of two methods, the back-propagation neural network method, which is widely used, and the heuristic method with MATLAB. In the training phase, the back-propagation method takes about 425 steps to convergence, while the heuristic method needs only 312 steps. In the fault diagnosis phase, the prediction accuracy of the heuristic method is 93.33%, while the back-propagation method scores 86.67%. It is concluded that the heuristic method can not only improve the convergence of the simulation but also significantly improve the prediction accuracy of the fault diagnosis system.

Keywords: photovoltaic diagnosis system; particle swarm optimization; back-propagation neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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