Issue |
EPJ Photovolt.
Volume 15, 2024
Special Issue on ‘Advances in French Photovoltaic Research in 2023’, edited by Mohamed Amara, Thomas Fix, Jean-Paul Kleider, Judikaël Le Rouzo and Denis Mencaraglia
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Article Number | 25 | |
Number of page(s) | 19 | |
DOI | https://doi.org/10.1051/epjpv/2024022 | |
Published online | 29 July 2024 |
https://doi.org/10.1051/epjpv/2024022
Original Article
Photovoltaic failure diagnosis using imaging techniques and electrical characterization
1
Centre d'Études et de Recherche de Djibouti, Laboratoire des Energies Nouvelles et Renouvelables, PO box: 486, Djibouti, Djibouti
2
Heliocity SAS, 31 rue Gustave Eiffel, 38000 Grenoble, France
3
LOCIE UMR 5271, Université Savoie Mont Blanc, CNRS, Solar Academy Graduate School, INES, 73376 Le Bourget-du-Lac, France
4
CSTB, 24 Rue Joseph Fourier, 38400 Saint-Martin-d'Hères, France
5
Univ Paris Est Créteil, CERTES, IUT de Sénart-Fontainebleau, 36 rue Georges Charpak, F-77567 Lieusaint, France
* e-mail: daha.enea@gmail.com
Received:
18
March
2024
Accepted:
19
June
2024
Published online: 29 July 2024
Inspections of 48 photovoltaic (PV) modules within a 302.4 kWp solar array were undertaken to expose the presence of defects after 12 years of operation under the harsh environmental conditions of Djibouti. To this end, a multiple-technique testing protocol was conducted including visual inspection (VI), infrared thermography (IR), current-voltage curve characterization (I-V), ultraviolet fluorescence (UVFL) and electroluminescence imaging (EL). The main visible degradation features observed were discoloration, bubbling and snail trails with occurrences of 100%, 93.7% and 2.1% respectively. According to the IR imaging results, hotspots were observed on cells affected by snail trails. IR was combined with convolutional neural network (CNN) techniques to automatically detect the different classes of failures that PV modules may experience. EL imaging reveals that the cracks of the cells underlie the observed snail trails during visual inspection and UVFL imaging. In addition, a decrease in STC power was observed after 12 yr of operation with a median reaching 5.5% corresponding to an average degradation rate of 0.46%/years. Conclusively, fault diagnosis with combined approaches of imaging and electrical techniques is crucial to prevent defects and minimize the investment losses; this will ensure uninterrupted power generation, extended service life and high safety of photovoltaic modules.
Key words: Photovoltaic fault diagnosis / infrared imaging / ultraviolet fluorescence / electroluminescence / current-voltage (I-V) curves / convolutional neural network
© D.H. Daher et al., Published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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