Modelling False Data Injection Attacks Against Non-linear State Estimation in AC Power Systems

Abstract

False data injection (FDI) attacks can disrupt the operation of the smart grid by manipulating the state estimation process without being detected. To deal with these kinds of attacks on smart grid networks, vulnerability analysis should carefully be developed under realistic conditions. Existing research efforts on FDI attacks have not performed the vulnerability analysis of the smart grid using the AC state estimation without incurring significant computational complexity. In this paper, we develop a low-complexity system to model the least-effort FDI attacks in the AC power grid. We do that by using a reduced row echelon (RRE) form-based greedy method on the AC state estimation process to compute the minimum number of measurements an attacker needs to compromise to launch the undetectable low-cost FDI attack with more efficiency. Simulation results obtained for various IEEE standard test systems show the efficient performance and enhanced accuracy of our proposed approach for modeling least-effort attacks.