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Adaptive automatic solar cell defect detection and classification based on absolute electroluminescence imaging

Author

Listed:
  • Wang, Youyang
  • Li, Liying
  • Sun, Yifan
  • Xu, Jinjia
  • Jia, Yun
  • Hong, Jianyu
  • Hu, Xiaobo
  • Weng, Guoen
  • Luo, Xianjia
  • Chen, Shaoqiang
  • Zhu, Ziqiang
  • Chu, Junhao
  • Akiyama, Hidefumi

Abstract

Current defect inspection methods for photovoltaic (PV) devices based on electroluminescence (EL) imaging technology lack juggling both labor-saving and in-depth understanding of defects, restricting the progress towards yield improvement and higher efficiency. Herein, we propose an adaptive approach for automatic solar cell defect detection and classification based on absolute EL imaging. Specifically, we first develop an unsupervised algorithm to automatically detect defects referring to the defect features in EL images. Then a diagnosis approach is proposed, which statistically classifies the detected defects based on the electrical origin. To the best of our knowledge, the proposed method is the first effort to integrate automatic defect detection with fine-grained classification. Experimental results on multiple types of solar cells show that the proposed method can achieve the average uncertainty of 5.15% at the minimum, with by up to 98.90% optimization ratio compared with two conventional methods. The proposed method is expected to provide more guiding feedback in both practical design and reliability diagnosis of the PV industry.

Suggested Citation

  • Wang, Youyang & Li, Liying & Sun, Yifan & Xu, Jinjia & Jia, Yun & Hong, Jianyu & Hu, Xiaobo & Weng, Guoen & Luo, Xianjia & Chen, Shaoqiang & Zhu, Ziqiang & Chu, Junhao & Akiyama, Hidefumi, 2021. "Adaptive automatic solar cell defect detection and classification based on absolute electroluminescence imaging," Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:energy:v:229:y:2021:i:c:s0360544221008550
    DOI: 10.1016/j.energy.2021.120606
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    References listed on IDEAS

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    1. Akram, M. Waqar & Li, Guiqiang & Jin, Yi & Chen, Xiao & Zhu, Changan & Zhao, Xudong & Khaliq, Abdul & Faheem, M. & Ahmad, Ashfaq, 2019. "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, Elsevier, vol. 189(C).
    2. Sharma, Vikrant & Chandel, S.S., 2013. "Performance and degradation analysis for long term reliability of solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 753-767.
    3. Din-Chang Tseng & Yu-Shuo Liu & Chang-Min Chou, 2015. "Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, October.
    4. Hu, Xiaobo & Chen, Tengfei & Hong, Jianyu & Chen, Shaoqiang & Weng, Guoen & Zhu, Ziqiang & Chu, Junhao, 2019. "Diagnosis of GaAs solar-cell resistance via absolute electroluminescence imaging and distributed circuit modeling," Energy, Elsevier, vol. 174(C), pages 85-90.
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    Cited by:

    1. Waqar Akram, M. & Li, Guiqiang & Jin, Yi & Chen, Xiao, 2022. "Failures of Photovoltaic modules and their Detection: A Review," Applied Energy, Elsevier, vol. 313(C).
    2. Chiwu Bu & Tao Liu & Tao Wang & Hai Zhang & Stefano Sfarra, 2023. "A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images," Energies, MDPI, vol. 16(9), pages 1-13, April.
    3. Tang, Wuqin & Yang, Qiang & Dai, Zhou & Yan, Wenjun, 2024. "Module defect detection and diagnosis for intelligent maintenance of solar photovoltaic plants: Techniques, systems and perspectives," Energy, Elsevier, vol. 297(C).

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