Fault diagnosis of a photovoltaic system using recurrent neural networks - Reda Djeghader; Ilyes Louahem Msabah; Samia Benzahioul; Abderrezak Metatla (Institute of Advanced Engineering and Science, 2022)
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.