Statistical structure learning of smart grid for detection of false data injection

Abstract

Although synchronous PMUs are being deployed across the grid, it is not economical to place them at every node. Therefore, at some nodes in the system state estimators will be used. Both PMUs and state estimators are prone to false data injection attacks. Thus, it is crucial to have a mechanism for fast and accurate detection of malicious tampering; both for preventing the attacks that may lead to blackouts, and for routine monitoring and control tasks of smart grid. We propose a decentralized false data injection detection scheme based on Markov graph of bus phase angles. We utilize Conditional Covariance Test (CCT) to learn the structure of smart grid. Using the DC power flow model, we show that under normal circumstances, and because of walk-summability of the grid graph, the Markov graph of voltage angles matches the power grid graph; otherwise, a discrepancy should trigger the alarm. Local grid topology is available online from the protection system and we exploit it to check for mismatch. Our method can detect the most recent stealthy deception attack on power grid that assumes knowledge of bus-branch model of the system and is capable of deceiving the state estimator. Specifically, under the stealthy deception attack, the Markov graph of phase angles changes. To the best of our knowledge, our remedy is the first to comprehensively detect this sophisticated attack and it does not need additional hardware. Moreover, our detection scheme is successful no matter the size of the attacked subset. Simulation of various power networks confirms our claims.