Statistical Structure Learning to Ensure Data Integrity in Smart Grid

Abstract

Robust control and maintenance of the grid relies on accurate data. 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 an agent maliciously tampering with the data-for both preventing attacks that may lead to blackouts, and for routine monitoring and control tasks of current and future grids. We propose a decentralized false data injection detection scheme based on Markov graph of the bus phase angles. We utilize the Conditional Covariance Test (CCT) to learn the structure of the 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 the voltage angles can be determined by the power grid graph. Therefore, a discrepancy between calculated Markov graph and learned structure should trigger the alarm. Local grid topology is available online from the protection system and we exploit it to check for mismatch. Should a mismatch be detected, we use correlation anomaly score to detect the set of attacked nodes. Our method can detect the most recent stealthy deception attack on the power grid that assumes knowledge of bus-branch model of the system and is capable of deceiving the state estimator, damaging power network observatory, control, monitoring, demand response and pricing schemes [12]. Specifically, under the stealthy deception attack, the Markov graph of phase angles changes. In addition to detect a state of attack, our method can detect the set of attacked nodes. 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.

A Novel Covert Agent for Stealthy Attacks on Industrial Control Systems Using Least Squares Support Vector Regression

“…It was assumed that the attacker knew the detailed system parameters. Such assumption can also be found in the recent work of Teixeira et al [9], Sedghi and Jonckheere [10], Manandhar et al [11], and Dutta and Langbort [12]. In particular, in [9], the authors also considered a more moderate scenario where the attacker's model knowledge contains some uncertainties.…”

Joint Estimation of Topology and Injection Statistics in Distribution Grids With Missing Nodes

“…For available line measurements, [4] uses maximum likelihood tests for estimating the operational topology using cycles basis. For available nodal voltage measurements, [5], [6], [7], [8], [9] use properties of the graphical model of voltage measurements to identify the operational topology. Similarly, properties of graphical models in dynamical systems that represent swing dynamics in power grids have been used in grid identification in [10], [11].…”

“…If voltage phase angles are also available, the injection statistics at all nodes can be computed by inverting Eqs. (5) or iteratively from leaves to the root using Eq. (9a) described later.…”

Inverter Probing for Power Distribution Network Topology Processing

“…The previous scheme has been improved by waiving the assumption on identical resistance-to-reactance ratios across lines [3], and by further utilizing prior information on power injection covariances at terminal buses [4], [5]. Topology identification has also been tackled using graphical models by exploiting the mutual information of voltage data [6], or by inspecting the entries of the voltage covariance matrix [7], [8]. A Wiener filtering approach using wide-sense stationary processes on radial networks is reported in [9].…”