To address the problem of low resolution in reconstructed image due to the lack of consideration for local texture features and global temperature distribution in traditional super-resolution reconstruction methods for infrared images of power equipment, a super-resolution reconstruction method based on Transformer-GAN aggregation network is proposed. Firstly, a local feature extraction module for power equipment is designed based on shift convolution, which expands the receptive field of the convolution without increasing parameters, extracting local texture and surrounding spatial dimension features of the power equipment. Then, a global feature extraction module is introduced to capture the correlation of temperature distributions between different regions of the image through deep convolution and spatial attention mechanisms. Finally, the UNet encoder-decoder network is used to fuse the local features and global representations at each layer, generating clear and natural infrared images of power equipment. The case study results show that the proposed method outperforms other methods in terms of peak signal to noise ratio (PSNR), structural similarity (SSIM), and visual information fidelity (VIF). It also has good subjective visual effects and strong generalization ability.