Dynamic PMU Configuration for Stealthy Multi-Snapshot FDIA Mitigation: Enhancing Security in Hybrid-Measurement-Based State Estimation in Power Systems

Modern power systems have evolved into cyber-physical power systems (CPPSs) requiring high-resolution spatial-temporal state awareness, driving the adoption of hybrid measurement-based state estimation (SE). However, such advancements introduce heightened cybersecurity requirements that demand systematic investigations from adversarial perspectives. This paper establishes a comprehensive attacker‒defender framework with dual contributions. From the attacker's perspective, this paper proposes a multi-snapshot coordinated false data injection attack (MS-FDIA) strategy against hybrid measurement-based SE. The proposed attack model can effectively bypass both hybrid measurement synchronization checks and temporal data anomaly detection mechanisms, thereby achieving enhanced stealth and operational impact. From the defender’s perspective, this paper develops a dynamic trusted phasor measurement unit (PMU) configuration scheme that adaptively reconfigures secure measurement matrix subsets against evolving multistage attacks. The defense mechanism is solved through two complementary approaches: a numerical algorithm based on matrix row transformations and a deep reinforcement learning (DRL)-based optimization method with temporal awareness. Comprehensive case studies on different standard test systems demonstrate the improved MS-FDIA's evasion capabilities and the defense scheme's effectiveness, and the test results reveal the matrix transformation algorithm's superiority in small-scale systems versus DRL's scalability advantages for large networks.