A False Data Injection Attack Approach Without Knowledge of System Parameters Considering Measurement Noise
Haosen Yang, Ziqiang Wang
Abstract
Due to the potential devastating impact on modern Internet-of-Things (IoT) integrated power grids, thefalse data injection attack (FDIA) has become a major concern. This article proposes an FDIA approach against state estimation without the knowledge of system parameters considering measurement noise. The proposed approach is able to mitigate the impact of measurement noise by utilizing the low-rank characteristic of the measurement data matrix, and can recover partial singular vectors of the state estimation Jacobian matrix (SEJM), based on which an unobservable attack can be launched. Besides, the scenario that only partial sensors can be tampered is investigated, and a matrix extension or split strategy is used to modify the matrix size, which makes the proposed method can be applied into the power grid of arbitrary scale. The presented method is capable of achieving a higher attack successful rate as well as requiring less amount of measurement data. Numerous cases demonstrate the effectiveness and advantages of the proposed method over other FDIA approaches in the scenario system parameters are unavailable.