The common defects observed on the photovoltaic cells during the manufacturing process include chipping, tree crack, micro-line, soldering, and short circuits. Most of the defects mentioned above are not directly visible, making it harder for visual inspection. An appropriate method for defect classification would be by performing electroluminescence (EL) imaging, which helps reveal the defects, making it possible to visualize cracks and helps evaluate the quality of photovoltaic (PV) modules. The phenomenon where light emission occurs when current passes through PV cells is electroluminescence. The manual analysis of these electroluminescence images can be time-consuming and needs expert knowledge of various defects. This paper explains the automatic defective solar mono-cell classification task executed with different classifiers of machine learning and deep learning along with necessary image preprocessing techniques used to enhance the detection results. In comparison with the machine learning approach, deep learning offers better results on dataset of 1840 solar cell images. CNN models gave an average accuracy of 92%, and the highest accuracy of 97% was obtained with VGG16 transfer learning models after fine-tuning.


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    Title :

    Comparison of Various Machine Learning and Deep Learning Classifiers for the Classification of Defective Photovoltaic Cells


    Additional title:

    Lect. Notes Electrical Eng.



    Conference:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Publication date :

    2023-11-18


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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