Federated Learning for Real-Time State Estimation and Dynamic Phasor Analysis in Wide-Area Power Grids

In the evolving landscape of power systems, real-time state estimation and dynamic phasor analysis are paramount for ensuring grid stability and reliability. This paper introduces a novel approach leveraging Federated Learning (FL) to achieve these objectives in wide-area power grids. Unlike traditional centralized learning methods, FL enables on-device training, ensuring data privacy and reducing the need for data transmission across the grid. Our methodology integrates FL with advanced state estimation techniques and phasor measurement units (PMUs) to provide real-time insights into grid dynamics. The proposed framework was tested on a benchmark power system model, demonstrating significant improvements in estimation accuracy and computational efficiency compared to conventional methods. Furthermore, the inherent decentralized nature of FL enhances the resilience of the estimation process against localized failures and cyber-physical threats. This research not only underscores the potential of FL in revolutionizing power grid analytics but also paves the way for its integration in other critical infrastructure systems.