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This research aims to develop and evaluate PVDefectNet, an AI-driven method for detecting and classifying faults in photovoltaic systems.

March 13, 2026Scientific ReportsOpen Access

AI-driven fault detection and classification in photovoltaic systems using deep learning techniques

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This research aims to develop and evaluate PVDefectNet, an AI-driven method for detecting and classifying faults in photovoltaic systems.
Utilized a resnet architecture for deep learning-based fault detection and classification.
Implemented data augmentation techniques to improve model robustness.
Conducted a five-stage process including data preparation, model design, training, evaluation, and performance analysis.
Applied Grad-CAM for visualizations to interpret model focus on defect areas.
Achieved an average accuracy of 98% with high precision (97.1), recall (96.5), and F1-score (96.8).
Demonstrated improved performance compared to existing methods in fault detection.
Visualizations indicated that the model effectively highlighted significant defect areas, enhancing interpretability.

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Study demonstrates high classification performance in photovoltaic systems, indicating effective monitoring solutions.

Talaat et al. (2026) studied this question.

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