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A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Author

Listed:
  • Cheng Yang

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Fuhao Sun

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Yujie Zou

    (Shanghai Zhabei Power Plant of State Grid Corporation of China, Shanghai 200432, China)

  • Zhipeng Lv

    (Energy Internet Research Institute Co., Ltd., State Grid Corporation of China, Shanghai 200437, China)

  • Liang Xue

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Chao Jiang

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Shuangyu Liu

    (Shanghai Guoyun Information Technology Co., Ltd., Shanghai 201210, China)

  • Bochao Zhao

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Haoyang Cui

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

Abstract

Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

Suggested Citation

  • Cheng Yang & Fuhao Sun & Yujie Zou & Zhipeng Lv & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Haoyang Cui, 2024. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods," Energies, MDPI, vol. 17(4), pages 1-37, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:837-:d:1336778
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    References listed on IDEAS

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    1. Gao, Han & Fan, Siyuan & He, Mingyue & Wang, Yu & Hong, Wenpeng & Ding, Wei, 2026. "CrackNet: A transformer-based approach for detecting microcrack in photovoltaic panels based on electroluminescence images," Renewable Energy, Elsevier, vol. 256(PH).

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