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Deep Learning-Powered PV System Fault Identification and Categorization

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The developed CNN-based model improved anomaly classification within photovoltaic systems.
Targeted preprocessing improved accuracy with metrics including AUC-PR and F1-score.
Use of infrared thermal imagery and advanced data augmentation addressed class imbalance.
Results indicate enhanced diagnostics for solar panel monitoring emphasizes data quality.

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Analysis uses deep learning to enhance fault identification in photovoltaic systems, suggesting improved diagnostics.

Dekhandji et al. (2025) studied this question.

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