Data-driven attacks and data recovery with noise on state estimation of smart grid

In this paper, we focus on the false data injection attacks (FDIAs) on state estimation and corresponding countermeasures for data recovery in smart grid. Without the information about the topology and parameters of systems, two data-driven attacks (DDAs) with noisy measurements are constructed, which can escape the detection from the residue-based bad data detection (BDD) in state estimator. Moreover, in view of the limited energy of adversaries, the feasibility of proposed DDAs is improved, such as more sparse and low-cost DDAs than existing work. In addition, a new algorithm for measurement data recovery is introduced, which converts the data recovery problem against the DDAs into the problem of the low rank approximation with corrupted and noisy measurements. Especially, the online low rank approximate algorithm is employed to improve the real-time performance. Finally, the information on the 14-bus power system is employed to complete the simulation experiments. The results show that the constructed DDAs are stealthy under BBD but can be eliminated by the proposed data recovery algorithms, which improve the resilience of the state estimator against the attacks.