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A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images

Author

Listed:
  • Chiwu Bu

    (School of Light Industry, Harbin University of Commerce, Harbin 150028, China)

  • Tao Liu

    (School of Light Industry, Harbin University of Commerce, Harbin 150028, China)

  • Tao Wang

    (Intelligent Manufacturing Engineering Department, Zibo Technician College, Zibo 255000, China)

  • Hai Zhang

    (Centre for Composite Materials and Structures (CCMS), Harbin Institute of Technology, Harbin 150001, China)

  • Stefano Sfarra

    (Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, Piazzale E. Pontieri 1, Monteluco di Roio, 67100 L’Aquila, AQ, Italy)

Abstract

Photovoltaic (PV) cells are a major part of solar power stations, and the inevitable faults of a cell affect its work efficiency and the safety of the power station. During manufacturing and service, it is necessary to carry out fault detection and classification. A convolutional-neural-network (CNN)-architecture-based PV cell fault classification method is proposed and trained on an infrared image data set. In order to overcome the problem of the original dataset’s scarcity, an offline data augmentation method is adopted to improve the generalization ability of the network. During the experiment, the effectiveness of the proposed model is evaluated by quantifying the obtained results with four deep learning models through evaluation indicators. The fault classification accuracy of the CNN model proposed here has been drawn by the experiment and reaches 97.42%, and it is superior to that of the models of AlexNet, VGG 16, ResNet 18 and existing models. In addition, the proposed model has faster calculation, prediction speed and the highest accuracy. This method can well-identify and classify PV cell faults and has high application potential in automatic fault identification and classification.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3749-:d:1134795
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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. Haiyong Chen & Yue Pang & Qidi Hu & Kun Liu, 2020. "Solar cell surface defect inspection based on multispectral convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 453-468, February.
    3. Meng-Hui Wang & Zong-Han Lin & Shiue-Der Lu, 2022. "A Fault Detection Method Based on CNN and Symmetrized Dot Pattern for PV Modules," Energies, MDPI, vol. 15(17), pages 1-17, September.
    4. Tsanakas, John A. & Ha, Long & Buerhop, Claudia, 2016. "Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 695-709.
    5. Giovanni Cipriani & Antonino D’Amico & Stefania Guarino & Donatella Manno & Marzia Traverso & Vincenzo Di Dio, 2020. "Convolutional Neural Network for Dust and Hotspot Classification in PV Modules," Energies, MDPI, vol. 13(23), pages 1-17, December.
    6. 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).
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