Dynamic Spatiotemporal Measurement Uncertainty-Aware Detection for Robust Power System State Estimation Against Stealthy FDIA

In cyber-physical power systems, state estimation (SE) serves as a critical function for operational monitoring but is increasingly vulnerable to functional failures induced by stealthy external attacks. To overcome the limitations of traditional attack detection methods for SE, particularly their heavy reliance on prior attack models and static measurement infrastructures, this paper proposes a robust SE model that proactively introduces dynamic spatiotemporal measurement uncertainty (DSTMU) to detect stealthy false data injection attacks (FDIAs) without requiring an explicit prior attack model, thus improving the resilience and security of power system operations. First, to disrupt the attacker's static perception of the measurement configuration, a robust SE model with structural uncertainty and adversarial resilience is constructed by dynamically reconfiguring the measurement sampling pattern via spatial selection and temporal weighting matrices, leveraging the inherent redundancy of hybrid SCADA and WAMS measurements. Subsequently, a consistency-based detection index, the random measurement selection consistency index (RMSCI), is designed to quantify the deviations in SE results caused by varying measurement configurations. The statistical characteristics of RMSCI are analytically derived under normal and adversarial conditions, verifying its sensitivity to stealthy attacks. Furthermore, an offline robust detection-oriented optimization model is formulated, jointly optimizing the measurement selection strategies and detection thresholds. A Benders decomposition-based algorithm is employed to achieve efficient offline solution convergence for the detection-oriented deployment problem. Finally, simulation results demonstrate that the proposed method can effectively detect FDIAs of varying intensities without requiring additional hardware or external detection modules.