Collaborative Detection for Security Threats to Distributed Generator Regulation Based on Semi-supervised Federated Learning

A collaborative detection method for regulation security threats to distributed generator based on semi-supervised federated learning is proposed to address the prominent issues of low detection accuracy and low communication efficiency in existing distributed generator regulation systems, as well as the objective reasons for the high cost and low efficiency of manual data annotation and insufficient data utilization caused by untrustworthy model automatic pseudo annotation. The method involves collaborative training between cloud and edge devices, and self-learning and optimization of the model are realized through unlabeled data to better adapt to the security threat environment of distributed generator regulation systems. Firstly, an improved Transformer model is used to effectively capture security threats. Secondly, considering the cross-device and cross-regional characteristics of data in distributed generator regulation systems, federated learning is introduced to ensure the privacy and security of local data. To address the issue of unlabeled data, a global model is obtained through cloud-edge collaborative training for pseudo labeling, and a loss function for consistency regularization and information entropy regularization is designed to ensure the credibility of pseudo labeling. Finally, a dynamic weighted aggregation method is designed to optimize parameter updates and model training. Simulation experiments are conducted on the power system dataset at the University of Mississippi, and the experimental results show that compared to FedAvg-FixMatch and FedMatch methods, the proposed method improved the detection accuracy by 8% and 4%, respectively, and both category recall and accuracy are improved, significantly reducing communication overhead by 18%~28%. This demonstrates the effectiveness and practicality of proposed method in security threat detection in distributed generator regulation systems. © 2024 Automation of Electric Power Systems Press. All rights reserved.