Research on fault diagnosis of photovoltaic panels based on deep learning image recognition technology

Chen Li'na; Bian Hui; Ren Mengmeng; Ma Fanlin; Zuo Tian; Li Jing

doi:10.1117/12.3026870

27 March 2024 Research on fault diagnosis of photovoltaic panels based on deep learning image recognition technology

Chen Li'na, Bian Hui, Ren Mengmeng, Ma Fanlin, Zuo Tian, Li Jing

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131050H (2024) https://doi.org/10.1117/12.3026870
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In order to achieve high accuracy identification of photovoltaic panel faults, a photovoltaic panel fault diagnosis method based on deep learning image recognition technology is proposed. Firstly, the residual network was introduced into the convolutional neural network (CNN) to obtain the residual convolution networks (ResNet) required for the study, and then the ResNet was trained by using the photovoltaic panel fault image dataset to determine the model parameters, and then the photovoltaic panel failure research was carried out. Comparative experiments verify the effectiveness of the proposed ResNet-based fault diagnosis model for photovoltaic panels, which shows that the proposed model has strong generalization and robustness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chen Li'na, Bian Hui, Ren Mengmeng, Ma Fanlin, Zuo Tian, and Li Jing "Research on fault diagnosis of photovoltaic panels based on deep learning image recognition technology", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131050H (27 March 2024); https://doi.org/10.1117/12.3026870

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KEYWORDS

Solar cells

Performance modeling

Education and training

Data modeling

Deep learning

Convolutional neural networks

Feature extraction

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