Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I

Research Article

Defect Detection of Photovoltaic Modules Based on Convolutional Neural Network

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  • @INPROCEEDINGS{10.1007/978-3-319-73564-1_13,
        author={Mingjian Sun and Shengmiao Lv and Xue Zhao and Ruya Li and Wenhan Zhang and Xiao Zhang},
        title={Defect Detection of Photovoltaic Modules Based on Convolutional Neural Network},
        proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I},
        proceedings_a={MLICOM},
        year={2018},
        month={2},
        keywords={Convolutional Neural Network PV module cracks Defect detection Deep learning},
        doi={10.1007/978-3-319-73564-1_13}
    }
    
  • Mingjian Sun
    Shengmiao Lv
    Xue Zhao
    Ruya Li
    Wenhan Zhang
    Xiao Zhang
    Year: 2018
    Defect Detection of Photovoltaic Modules Based on Convolutional Neural Network
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73564-1_13
Mingjian Sun1,*, Shengmiao Lv1, Xue Zhao1, Ruya Li1, Wenhan Zhang1, Xiao Zhang1
  • 1: Harbin Institute of Technology at Weihai
*Contact email: sunmingjian@hit.edu.cn

Abstract

Deep learning is employed to detect defects in photovoltaic (PV) modules in the thesis. Firstly, the thesis introduces related concepts of cracks. Then a convolutional neural network with seven layers is constructed to classify the defective battery panels. Finally, the accuracy of the validation set is 98.35%. Besides, the thesis introduces a method in which a single battery cell can be extracted from the Electro Luminescence (EL) image of the PV module. This method is very suitable for automatic inspection of photovoltaic power plants.