Due to the many types of electrical equipment and the complex connection between the equipment in the transformer station, there are many common problems that the location of equipment and picture contrast are relatively single, a limited number of target images and markers can be obtained in practical applications, and the lack of accuracy of electrical equipment image segmentation is brought by the traditional way of segmentation. In this paper, CNN (Convolutional Neural Network) is combined with Transformer to form a new model for segmentation of electrical equipment, and a new SE-Transfomer (Substation Equipment Transformer) network based on codec structure is proposed. To obtain the local context information, the coder extracts the spatial feature map by means of CNN at first. Meanwhile, the feature map is carefully modified with multi-scale feature inputs for global feature modeling. The decoder extracts global deep features using Transformer and performs stepwise up-sampling to predict the detailed segmentation map.SE-Transfomer is extensively experimented on the dataset of Liangjiazhuang Transformer Station in Shanxi province, and its longitudinal results of Dice , Recall, Specificity and RMSE (Root Mean Square Error) are 89.31%, 90.52%, 89.62%, and 11.32, respectively. This result indicates that SE-Transfomer obtains comparable or higher results than previous state-of-the-art segmentation methods on the scanning of electrical equipment in the transformer station.