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<p>Safe and efficient operation of photovoltaic (PV) solar panels depends on early defect detection during manufacturing. ‘Bright spots’ on Electro-Luminescence (EL) images of Photovoltaic (PV) solar panels are critical defects, leading to excess energy production, short circuits, overheating, and potential fires. After extensive benchmarking against state-of-the-art methods, this paper proposes a robust approach for reliable bright spot detection based on image classification using novel features and synthetic bright spot EL images generated by generative adversarial networks (GANs). The proposed approach achieved an area under the precision-recall curve (AUPRC) of 0.9526 signifying its real-world applicability for reliable bright spot detection in PV solar panels.</p>

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Accurate detection of bright spots in electro-luminescence images of photovoltaic panels

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月6日

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  • Rahul Nair,
  • Anand Thirwani,
  • Kedar Kulkarni

摘要

Safe and efficient operation of photovoltaic (PV) solar panels depends on early defect detection during manufacturing. ‘Bright spots’ on Electro-Luminescence (EL) images of Photovoltaic (PV) solar panels are critical defects, leading to excess energy production, short circuits, overheating, and potential fires. After extensive benchmarking against state-of-the-art methods, this paper proposes a robust approach for reliable bright spot detection based on image classification using novel features and synthetic bright spot EL images generated by generative adversarial networks (GANs). The proposed approach achieved an area under the precision-recall curve (AUPRC) of 0.9526 signifying its real-world applicability for reliable bright spot detection in PV solar panels.