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

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.