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

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.

Automated summary

A convolutional-neural-network-based PV cell fault classification method is proposed in this study, trained on an infrared image data set. An offline data augmentation method is adopted to improve the network's generalization ability. The experiment shows that the proposed model achieves a fault classification accuracy of 97.42%, surpassing the performance of existing models such as AlexNet, VGG 16, and ResNet 18, with faster calculation and prediction speed. This method has high application potential in automatic fault identification and classification of PV cells.