Substation Abnormal Scene Recognition Based on Two-Stage Contrastive Learning

Substations are an important part of the power system, and the classification of abnormal substation scenes needs to be comprehensive and reliable. The abnormal scenes include multiple workpieces such as the main transformer body, insulators, dials, box doors, etc. In this research field, the scarcity of abnormal scene data in substations poses a significant challenge. To address this, we propose a few-show learning algorithm based on two-stage contrastive learning. In the first stage of model training, global and local contrastive learning losses are introduced, and images are transformed through extensive data augmentation to build a pre-trained model. On the basis of the built pre-trained model, the model is fine-tuned based on the contrast and classification losses of image pairs to identify the abnormal scene of the substation. By collecting abnormal substation images in real scenes, we create a few-shot learning dataset for abnormal substation scenes. Experimental results on the dataset demonstrate that our proposed method outperforms State-of-the-Art few-shot learning algorithms in classification accuracy.