A novel federated learning framework for semantic segmentation of terminal block in smart substation

Abstract

Recent advancements in computer vision have significantly enhanced the intelligence operation and maintenance of substation equipment. In this paper, we advance this progress and focus on semantic segmentation of secondary screen cabinet terminal blocks in substations. We note that existing schemes are centralized, which may be unscalable, and more importantly, may be very difficult to protect data privacy. In response, we develop a novel semantic segmentation framework based on federated learning. This framework includes a federated learning system composed of a trusted third party, a cloud server, multiple power stations, and substations across various regions. To ensure substation security, our design incorporates anonymous identity verification managed by the trusted third party and other participants. Local substations then employ the designed semantic segmentation model to extract data and model elements through cameras and store them in distributed power stations. To address data heterogeneity in distributed semantic segmentation, we design a diffusion model for data augmentation and improve the feature similarity loss, which helps mitigate the local optima and enhance the global generalization capability of the final model. Experiments conducted using real data from multiple substations have demonstrated that our framework achieves an intelligent terminal block recognition system with an accuracy of 93.41% and mIoU of 81.37%.