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Energies 2017, 10(2), 226; doi:10.3390/en10020226

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

1
Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
2
College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
*
Author to whom correspondence should be addressed.
Academic Editors: Senthilarasu Sundaram and Tapas Mallick
Received: 23 November 2016 / Revised: 5 January 2017 / Accepted: 9 February 2017 / Published: 15 February 2017
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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 (Voc), short-circuit current (Isc), maximum power (Pm) and voltage at maximum power point (Vm) 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. View Full-Text
Keywords: photovoltaic diagnosis system; particle swarm optimization; back-propagation neural network photovoltaic diagnosis system; particle swarm optimization; back-propagation neural network
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Liao, Z.; Wang, D.; Tang, L.; Ren, J.; Liu, Z. A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network. Energies 2017, 10, 226.

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