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Foreign Object Shading Detection in Photovoltaic Modules Based on Transfer Learning

Author

Listed:
  • Bin Liu

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Qingda Kong

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Hongyu Zhu

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Dongdong Zhang

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Hui Hwang Goh

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Thomas Wu

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

Abstract

As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered by foreign objects in the environment such as leaves and bird droppings, resulting in a decrease in photoelectric conversion efficiency, power losses, and even the “hot spot” phenomenon, resulting in damage to the modules. Existing methods mostly inspect foreign objects manually, which not only incurs high labor costs but also hinders real-time monitoring. To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional block attention module, which can identify foreign object shading on the surfaces of PV modules in actual operating environments. This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules. The results show that the advanced model can significantly improve the target detection AP values.

Suggested Citation

  • Bin Liu & Qingda Kong & Hongyu Zhu & Dongdong Zhang & Hui Hwang Goh & Thomas Wu, 2023. "Foreign Object Shading Detection in Photovoltaic Modules Based on Transfer Learning," Energies, MDPI, vol. 16(7), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:2996-:d:1106972
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    References listed on IDEAS

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