Structural-Constrained Methods for the Identification of False Data Injection Attacks in Power Systems

Power system functionality is determined on the basis of power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as disruptions in electricity distribution, maintenance damage, and financial losses. Classical bad data detection (BDD) methods, developed to ensure PSSE reliability, are unable to detect well-designed attacks, named unobservable false data injection (FDI) attacks. In this paper, we develop novel structural-constrained methods for the detection of unobservable FDI attacks and the identification of the attacked buses. The proposed methods are based on formulating structural, sparse constraints on both the attack and the system loads. First, we exploit these constraints in order to compose an appropriate model selection problem. Then, we develop the associated generalized information criterion (GIC) for this problem. However, the GIC method's computational complexity grows exponentially with the network size, which may be prohibitive for large networks. Therefore, based on the proposed structural and sparse constraints, we develop two novel low-complexity methods for the practical identification of unobservable FDI attacks: 1) a modification of the state-of-the-art orthogonal matching pursuit (OMP) method; and 2) a method that utilizes the graph Markovian property in power systems, i.e. the second-neighbor relationship between the power data at the system's buses. In order to analyze the performance of the proposed methods, the appropriate oracle and clairvoyant detectors are also derived. The performance of the proposed methods is evaluated on the IEEE-30 bus test case.