LBSCA: Learning Real-time Power System State Estimation Under Hidden Adversarial Attacks

Abstract

Accurate power system state estimation (PSSE) is crucial for effective control and monitoring of power grids. However, existing techniques face numerous challenges including compromised measurements from cyber-attacks and the need for real-time operations. Traditional PSSE methods based on weighted least squares are susceptible to outliers and convergence issues, while convex relaxation techniques like semi-definite programming struggle with computational complexity. Recent deep learning approaches for PSSE are data-driven but unaware of the underlying physical constraints. This paper proposes a novel framework called learned block successive convex approximation (LBSCA) to address PSSE amidst hidden adversarial attacks. LBSCA utilizes an efficient block successive convex approximation algorithm that provably converges to a stationary point and can be learned via algorithm unrolling. The resulting deep neural network achieves real-time, end-to-end PSSE in fractions of a millisecond, even when attacks are unobserved and the network topology is incomplete. Evaluated on the IEEE 118-bus system under realistic noise profiles and stealthy directional and sparse injection attacks, LBSCA achieves lower state estimation errors by up to a factor of three than state-of-the-art deep learning baselines and delivers inference speeds up to four orders of magnitude faster than classical optimization algorithms. Our results demonstrate that model-driven unrolling of robust estimation yields both theoretical rigor and practical efficiency, underscoring its potential in improving real-time power system operation’s efficiency and security.