Solar ViT: Vision Transformer for Fault Detection in Solar PV Systems

  • Authors

    • Pushpa Makwane Government Holkar Science College, Indore, M.P., India
    • Yogesh Kumar GD Goenka University, Gurugram, Haryana, India
    • Alok Srivastava GD Goenka University, Gurugram, Haryana, India
    • Madhuri Gokhale Jabalpur Engineering College, Jabalpur, M.P., India
    • Saurabh Singh Jabalpur Engineering College, Jabalpur, M.P., India
    • Varsha Sisodiya State Street Corporation Pvt. Ltd., Bengaluru, India
    https://doi.org/10.14419/1h6tfk39

    Received date: May 18, 2025

    Accepted date: June 11, 2025

    Published date: June 30, 2025

  • Artificial Intelligence; Convolutional Neural Networks; Crack Detection; Vision Transformer; Photovoltaic

  • Abstract

    Clean, renewable, and sustainable energy is vital for advancing social, economic, and environmental well-being, ultimately fostering productivity and long-term development. As solar photovoltaic (PV) systems become a cornerstone in global efforts to combat climate change and promote environmental sustainability, ensuring their reliable operation is increasingly important. Artificial Intelligence (AI) and Deep Learning (DL) have emerged as powerful enablers, offering advanced capabilities in monitoring, fault detection, and predictive maintenance of solar energy systems. These intelligent technologies enhance operational efficiency, minimize downtime, and support sustainable energy management. Blending AI and renewable energy supports key Sustainable Development Goals like clean energy, innovation, and climate action, driving both technological progress and environmental protection. This paper introduces SolarViT, a Vision Transformer-based deep learning model designed for precise fault detection in solar PV panels. By detecting issues like micro cracks and hotspots early, SolarViT helps prevent efficiency loss and reduce maintenance costs. Leveraging transfer learning, data augmentation, and ensemble methods, it delivers robust diagnostics while addressing interpretability, efficiency, and real-time deployment. This supports smarter solar infrastructure and promotes broader adoption of clean energy for sustainable development.

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    Makwane , P. ., Kumar, Y. . ., Srivastava , A. ., Gokhale , M. ., Singh , S. ., & Sisodiya , V. . (2025). Solar ViT: Vision Transformer for Fault Detection in Solar PV Systems. International Journal of Basic and Applied Sciences, 14(2), 535-541. https://doi.org/10.14419/1h6tfk39