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.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Sun, M., Lv, S., Zhao, X., Li, R., Zhang, W., Zhang, X. (2018). Defect Detection of Photovoltaic Modules Based on Convolutional Neural Network. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_13
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DOI: https://doi.org/10.1007/978-3-319-73564-1_13
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