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Samia Benzahioul
University of Skikda
Electrical & Electronics Engineering

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Fault diagnosis of a photovoltaic system using recurrent neural networks Reda Djeghader; Ilyes Louahem Msabah; Samia Benzahioul; Abderrezak Metatla
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4295

Abstract

The developed work in this paper is a part of the detection and identification of faults in systems by modern techniques of artificial intelligence. In a first step we have developed amulti-layer perceptron (MLP), type neural network to detect shunt faults and shading phenomenon in photovoltaic (PV) systems, and in the second part of the work we developed anotherrecurrent neural network (RNN) type network in order to identify single and combined faults in PV systems. The results obtained clearly show the performance of the networks developed for the rapid detection of the appearance of faults with the estimation of their times as well as the robust decision to identify the type of faults in the PV system.
Co-Authors Abderrezak Metatla Ilyes Louahem Msabah Reda Djeghader