Risk Mitigation for Dynamic State Estimation Against Cyber Attacks and Unknown Inputs

TL;DR

This paper proposes a risk mitigation strategy using dynamic state estimation and attack detection to secure power grids against cyber-attacks and unknown inputs, ensuring system observability with real-time PMU data.

Contribution

It introduces a novel integrated approach combining state estimation, attack detection, and optimization for real-time risk mitigation in power systems.

Findings

Effective detection of cyber-attacks using the proposed algorithm

Successful mitigation of threats while maintaining system observability

Validated approach through case studies on power system models

Abstract

Phasor measurement units (PMUs) can be effectively utilized for the monitoring and control of the power grid. As the cyber-world becomes increasingly embedded into power grids, the risks of this inevitable evolution become serious. In this paper, we present a risk mitigation strategy, based on dynamic state estimation, to eliminate threat levels from the grid's unknown inputs and potential cyber-attacks. The strategy requires (a) the potentially incomplete knowledge of power system models and parameters and (b) real-time PMU measurements. First, we utilize a dynamic state estimator for higher order depictions of power system dynamics for simultaneous state and unknown inputs estimation. Second, estimates of cyber-attacks are obtained through an attack detection algorithm. Third, the estimation and detection components are seamlessly utilized in an optimization framework to determine the most impacted PMU measurements. Finally, a risk mitigation strategy is proposed to guarantee the elimination of threats from attacks, ensuring the observability of the power system through available, safe measurements. Case studies are included to validate the proposed approach. Insightful suggestions, extensions, and open problems are also posed.