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Detection of Defective Solar Panel Cells in Electroluminescence Images with Deep Learning

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  • Abdil Karakan

    (Dazkırı Vocational School, Afyon Kocatepe University, 03950 Afyonkarahisar, Türkiye)

Abstract

In this study, faults in solar panel cells were detected and classified very quickly and accurately using deep learning and electroluminescence images together. A unique and new dataset was created for this study. Monocrystalline and polycrystalline solar panel cells were used in the dataset. The dataset included intact, cracked and broken images for each solar panel cell. The dataset was preprocessed and multiplied to equalize the intact, cracked and broken numbers. Seven different deep learning architectures were used in this study. As a result of this study, 97.82% accuracy was achieved for the monocrystalline solar panel cells and 96.29% for the polycrystalline solar panel cells in the SqueezeNet architecture.

Suggested Citation

  • Abdil Karakan, 2025. "Detection of Defective Solar Panel Cells in Electroluminescence Images with Deep Learning," Sustainability, MDPI, vol. 17(3), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1141-:d:1580626
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    References listed on IDEAS

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    1. Pratt, Lawrence & Govender, Devashen & Klein, Richard, 2021. "Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation," Renewable Energy, Elsevier, vol. 178(C), pages 1211-1222.
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