In recent years, machine vision algorithms are widely used in substations to analyze the difference changes of multi-temporal inspection images, which are used to detect various substation equipment defects to ensure the safety of operation. However, due to the different shooting times, there are various interference changes such as weather, illumination and season between multi-temporal images, which pose challenges to the defect detection of substation equipment. Therefore, this paper presents an anti-interference defect detection method for substation equipment based on multi-temporal inspection images. Firstly, the style transfer model CycleGAN is utilized to learn the mapping between different style domains, then interference images with weather, light and seasonal interference changes are generated from the detected images. Secondly, we utilize the reference image, the detection image and the interference image to train a TripleNet cooperatively, and a spatial consistency loss is proposed to resist various interference changes at the feature level, which aims to extract the robust multi-scale difference features. Finally, a path aggregation network is built to fuse multi-scale difference features, which is utilized to get multi-scale defect detection results. The experimental verification is carried out on the multi-temporal inspection image dataset of actual substation equipment. Compared with the non siamese network and the general siamese network, the proposed method can improve the mAP by 2.09% and 0.67%, and the accuracy of original samples and interference samples is more balanced. The experiments demenstrate that the proposed method can improve the accuracy and enhance the anti-interference ability of the model for substation equipment defect detection.