Boundary Defense Against Cyber Threat for Power System State Estimation

Power grid operation is increasingly data-centric, raising reliability concerns under data attacks. We quantify and visualize non-robust regions for state estimation—graph-structured quadratic sensing—where local data manipulation can induce global estimation errors. We propose an optimization-based graphical boundary defense to localize manipulated regions, preventing local attacks from having global effects and enhancing situational awareness. The framework reveals key geometric and algebraic factors impacting robustness and is applied to the U.S. grid.