CJEE

Chinese Journal of Electrical Engineering ›› 2024, Vol. 10 ›› Issue (1): 3-11.doi: 10.23919/CJEE.2023.000043

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Defect Detection in c-Si Photovoltaic Modules via Transient Thermography and Deconvolution Optimization*

Zekai Shen1, Hanqi Dai2, Hongwei Mei3,*, Yanxin Tu3, Liming Wang3   

  1. 1. State Grid Hangzhou Power Supply Company,State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310000, China;
    2. Huairou Power Supply Branch, State Grid Beijing Electric Power Co., Ltd., Beijing 101400, China;
    3. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
  • Received:2023-08-03 Revised:2023-08-13 Accepted:2023-08-16 Online:2024-03-25 Published:2024-04-10
  • Contact: * E-mail: mei.hongwei@sz.tsinghua.edu.cn
  • About author:Zekai Shen received the B.S. degree in Electrical Engineering from the Department of Electrical Engineering, Tsinghua University, Beijing, China, in 2020 and the M.S. degree from the Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
    He currently works for the State Grid Hangzhou Power Supply Company in Hangzhou, China. His research interests include high-voltage and insulation technologies.
    Hanqi Dai received the B.S. degree from the School of Electrical Engineering of Wuhan University, Wuhan, China. In 2011, he received the M.S. degree from the Department of Electrical Engineering of Tsinghua University, and in 2014, he received a Ph.D. degree in High-voltage Engineering from the Department of Electrical Engineering, Tsinghua University.
    He currently works for the State Grid Beijing Electric Power Company in Beijing, China. His research interests include power system operations, planning, and high-voltage external insulation technologies.
    Hongwei Mei received the B.S. and M.S. degrees in Power Systems and Automation from the Department of Electrical Engineering, Harbin Institute of Technology, Harbin, China, in 2002 and 2004, respectively, and a Ph.D. degree in Electrical Engineering from the Department of Electrical Engineering, Tsinghua University, Beijing, China, in 2012.
    He is currently an Associate Professor with the Shenzhen International Graduate School, Tsinghua University, Shenzhen, China. His major research fields include high-voltage and insulation technologies.
    Yanxin Tu received the B.S. degree in Electrical Engineering and Automation from the Department of Electrical Engineering, Wuhan University, Wuhan, China, in 2017. He is currently working toward a Ph.D. in Electrical Engineering at Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
    His research interests include the condition monitoring and diagnostics of high-voltage insulation systems.
    Liming Wang received the B.S., M.S., and Ph.D. degrees in High-voltage Engineering from the Department of Electrical Engineering, Tsinghua University, Beijing, China, in 1987, 1990, and 1993, respectively. He has been working at Tsinghua University since 1993.
    His major research fields include high-voltage insulation and electrical discharge, flashover mechanisms on contaminated insulators, and the application of pulsed electric fields.
  • Supported by:
    * National Natural Science Foundation of China under Grant 51977117.

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Abstract: Defects may occur in photovoltaic (PV) modules during production and long-term use, thereby threatening the safe operation of PV power stations. Transient thermography is a promising defect detection technology; however, its detection is limited by transverse thermal diffusion. This phenomenon is particularly noteworthy in the panel glasses of PV modules. A dynamic thermography testing method via transient thermography and Wiener filtering deconvolution optimization is proposed. Based on the time-varying characteristics of the point spread function, the selection rules of the first-order difference image for deconvolution are given. Samples with a broken grid and artificial cracks were tested to validate the performance of the optimization method. Compared with the feature images generated by traditional methods, the proposed method significantly improved the visual quality. Quantitative defect size detection can be realized by combining the deconvolution optimization method with adaptive threshold segmentation. For the same batch of PV products, the detection error could be controlled to within 10%.

Key words: Photovoltaic module, transient thermography, point spread function, deconvolution optimization, quantitative detection