Research Article
Artificial Neural Network based fault diagnosis in an isolated photovoltaic generator
@INPROCEEDINGS{10.4108/eai.11-11-2021.2317977, author={Ousmane W. COMPAORE and Galeb HOBLOS and Zacharie KOALAGA}, title={Artificial Neural Network based fault diagnosis in an isolated photovoltaic generator}, proceedings={Proceedings of the 4th edition of the Computer Science Research Days, JRI 2021, 11-13 November 2021, Bobo-Dioulasso, Burkina Faso}, publisher={EAI}, proceedings_a={JRI}, year={2022}, month={5}, keywords={photovoltaic generator pvg fault classification detection diagnosis artificial neuron networks (ann) remaining useful life (rul)}, doi={10.4108/eai.11-11-2021.2317977} }- Ousmane W. COMPAORE
Galeb HOBLOS
Zacharie KOALAGA
Year: 2022
Artificial Neural Network based fault diagnosis in an isolated photovoltaic generator
JRI
EAI
DOI: 10.4108/eai.11-11-2021.2317977
Abstract
The efficiency of a photovoltaic (PV) system depends not only on environmental and operating conditions, but also on manufacturing. This dependence is intrinsically linked to parameters such as Rs, Rsh, Ncell or Iph. In other words, a good PV generator (PVG) is one where the power delivered by the PVG is maximum whatever the conditions of use. In this article, we expose a model of PVG, as well as some faults that affect its optimal functioning. Given the complexity and the multitude of diagnosis methods, we have opted for the artificial neural networks (ANN) approach to detect, identify and locate certain faults that hinder its good performance. Once the correct diagnosis is made, it will be up to the maintenance technicians to take the necessary actions.
