Artificial intelligence in photovoltaic fault diagnosis: A Natural Language-Based Topic-tSNE Fusion analysis
Résumé
Timely fault detection in photovoltaic systems is critical for ensuring energy efficiency, reliability, and cost-effectiveness. However, the nonlinear and weather-dependent behavior of photovoltaic systems poses challenges for accurate diagnosis. This study presents a large-scale review of 983 scientific publications on artificial intelligence-based photovoltaic fault detection, using a novel methodology called Topic-tSNE Fusion. This approach integrates topic modeling, dimensionality reduction, and expert analysis to extract and visualize dominant research themes. Four key machine learning paradigms are identified: supervised, unsupervised, semi-supervised, and reinforcement learning. Among them, supervised methods, particularly neural networks and support vector machines, are the most frequently applied, showing accuracies above 95% in controlled conditions. The analysis also reveals growing use of semi-supervised and hybrid approaches to overcome data scarcity. Commonly monitored variables include irradiance, voltage, and current, while the most studied faults are shading, open-circuit, and degradation. Several open-access datasets supporting fault diagnosis research are catalogued. Overall, the proposed method enables a more objective and scalable review process and uncovers emerging trends, such as the shift toward lightweight artificial intelligence for edge deployment and frugal diagnostic architectures. The methodology is scalable and adaptable to other domains facing similar challenges in knowledge synthesis and system monitoring.
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PV Panel: Irradiance, Temperature, Partial Shading — I–V Curves
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Cite 10.17632/n76t439f65.1 Jeu de données Bakdi, A. (2020). GPVS-Faults: Experimental Data for fault scenarios in grid-connected PV systems under MPPT and IPPT modes [Data set]. Mendeley. https://doi.org/10.17632/N76T439F65.1
GPVS-Faults
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Cite 10.17632/3fr92f4xy9.1 Jeu de données Rakeshkumar Mahto. (2022). Simulation Dataset of Partial Shading and Fault of a Photovoltaic Module [Data set]. Mendeley. https://doi.org/10.17632/3FR92F4XY9.1
Partial Shading in a Photovoltaic Module (simulation)
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Electrical Behavior of Photovoltaic Panels from RGB Images
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Cite 10.5281/zenodo.7868082 Jeu de données Manso Callejo, M. Á., & Calimanut-Ionut, C. (2023). SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.7868082
SPVPANELEX
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Cite 10.17632/mmwhkt7whb.3 Jeu de données Motahhir, S. (2020). MATLAB/Simulink Model of Photovoltaic Cell, Panel and Array [Data set]. Mendeley. https://doi.org/10.17632/MMWHKT7WHB.3
MATLAB/Simulink model of PV cell, panel and array
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Cite 10.21227/f0nc-hc38 Jeu de données Arnaudo, E. A. (2023). Piedmont Photovoltaic Panels Dataset [Data set]. IEEE DataPort. https://doi.org/10.21227/F0NC-HC38
Piedmont Photovoltaic Panels Dataset