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Physics-Informed State Estimation Model With Controllable Uncertainty: Enhancing Resilience Against False Data Injection Attacks in Power Systems
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DOI：10.1109/tii.2026.3681361.png)
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Abstract
En 中文
State estimation (SE) is vital for secure power system operation, but remains susceptible to stealthy false data injection attacks (FDIAs). Existing detection methods are primarily designed using prior attack knowledge, which limits their adaptability to unknown threats. This article introduces the concept of controllable uncertainty into SE, where uncertainty is actively regulated at inference time rather than passively induced by stochastic training effects, and proposes an attack-resilient SE model that enables active defense through an integrated estimation-detection-feedback mechanism. In the estimation stage, a spatiotemporal LSTM-GNN-based model equipped with Monte Carlo dropout executes multisample perturbation passes on identical inputs, whereas physics-consistency constraints enforce power-flow feasibility. In the detection stage, a detection index coupling mean shift and uncertainty expansion is introduced, with thresholds obtained via statistical calibration and perturbation-response bounds to ensure detectability without excessive false alarms. In the feedback stage, the dropout rate and the sampling density are adapted online in response to the detection statistics, amplifying attack signatures under anomalies and attenuating perturbations in normal scenarios to preserve estimation accuracy. Case studies on the standard test systems demonstrate consistently higher detection rates, lower false-alarm rates, and online latency compatible with operational requirements against single-snapshot, temporally optimal, and spatiotemporally coordinated FDIAs, without additional hardware investment. The results indicate that integrating physics consistency with actively controllable uncertainty offers a practical pathway toward enhancing the functional safety and resilience of SE.
Keywords:
Controllable uncertainty
detection
false data injection attacks (FDIAs)
Monte Carlo (MC) dropout
physics-consistency
power systems
state estimation (SE)
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