Partial discharge diagnosis via a novel federated meta-learning in gas-insulated switchgear.

Several deep learning partial discharge (PD) diagnostic approaches have been developed in recent years to guarantee the security and stability of gas-insulated switchgear (GIS). The centralized training method requires multiple clients to jointly obtain as much data as possible to train the model to achieve excellent performance, which is impractical due to conflicts of interest and privacy protection. Furthermore, because of differences in the distribution of client data and the presence of a small sample, achieving high-precision and robust diagnosis for each client is an urgent problem. To that end, a novel personalized federated meta-learning (FML) is proposed in this paper to address the aforementioned issues. It develops reliable and personalized PD diagnosis models by collaborating with multiple clients and solves the problem of small sample diagnosis through scenario training under the premise of protecting data privacy. The experimental results show that the FML proposed can diagnose GIS PD with high precision and robustness for each client while maintaining privacy. The diagnostic accuracy of the FML proposed in this paper, especially for on-site unbalanced small sample clients, is 93.07%, which is significantly higher than that for other methods. It serves as a model for the collaborative development of an effective GIS PD diagnostic model.