Detection of faults in photovoltaic arrays can reduce power generation losses and extend the equipment’s lifespan. Traditional operation and maintenance of photovoltaic power stations primarily rely on electrical characteristics or infrared images. However, data from a single modality are susceptible to environmental interference, affecting detection accuracy. To address these issues, we propose a model called PV-DETR for fault detection in photovoltaic arrays under complex environmental conditions. This model is an extension of RT-DETRv2, which leverages the Transformer architecture for feature extraction and decoding. The model employs a PResNet50 module instead of the original ResNet50, along with haar wavelet downsampling and a parallel block attention mechanism. The PResNet50 module can reduce dimensionality while minimizing information loss. Haar wavelet downsampling retains the original global information and compresses feature maps effectively, and the parallel block attention mechanism significantly enhances the detection of small infrared targets. Experimental results show that the final PV-DETR model achieves an average accuracy of 89% and an average recall of 85% in fault detection using multimodal data, outperforming existing models, including the original RT-DETRv2.

An infrared image segmentation model for photovoltaic arrays is proposed based on the improved Segformer. The inception-enhanced attention mechanism and multi-scale spatial feature extraction is leveraged to address problems, such as segmentation holes and environmental misclassification. Furthermore, the encoder of the proposed model is improved using the Feature Pyramid Network and bilinear interpolation operation to enhance the completeness of edge details. Experiments on infrared images gathered from a real-world power station shows that the model achieves improvements of 0.48 in mIoU, 0.3 in mAcc, and 1.38 in mDice, compared to existing models besides the original Segformer.