Intelligent fault detection in photovoltaic modules using attention-based deep learning network

Photovoltaic (PV) systems experience various faults due to environmental conditions, human errors, and equipment failure during their service life. To necessitate maximum power generation and ensure ideal operating conditions, the development of intelligent fault diagnosis models is essential. In the present study, an attention-based deep learning network, namely, vision transformer (ViT), is adopted to automatically detect the visual faults, such as glass breakage, discoloration, burn marks, snail trail, good panel, and delamination on PV modules. An image dataset has been developed with the true color images of various faulty PV modules. The ViT model was fine-tuned and trained over the custom dataset created. The trained ViT model demonstrates a superior classification accuracy of 99.84% for fault detection and classification in PV modules. The obtained classification results of the model are compared with several other classification results reported in the literature. The ViT model could potentially be integrated into existing inspection systems for autonomous, real-time, efficient, and robust condition monitoring of PV modules.