A Dynamic Reconfiguration-based Approach to Resilient State Estimation

Abstract

The increasing complexity and connectivity of power systems is making it increasingly likely that they will be subject to malicious attacks that compromise operation. Recent studies have shown that these systems are vulnerable to a wide range of cyber-attacks, including False Data Injection (FDI). Conventional security monitoring and protection tools are based on passive defense strategies. In this paper, we propose an approach for active defense that improves system security and the FDI attack detection rate. The key insight for this approach is that emerging micro-grids can utilize distributed energy resources to dynamically reconfigure the system (e.g. current flow paths), across multiple acceptable configurations. Instead of using information from only a single configuration to detect FDI attacks, our proposed approach uses dynamic reconfiguration to compare measured and estimated states under multiple configurations to accurately detect FDI attacks. We evaluate our approach in the specific scenario of emerging micro-grids. We develop a novel technique for state estimation using multiple configurations and demonstrate that this approach significantly improves FDI detection accuracy.