Applying the Mahalanobis Distance to Develop Robust Approaches Against False Data Injection Attacks on Dynamic Power State Estimation.

Although many researchers have studied false data injection (FDI) attacks in power state estimation, current state estimation approaches are still highly vulnerable to FDI attacks. In this paper, we investigate the problem of the above three FDI attacks against dynamic power state estimation (DSE). In this research, we propose two robust defense approaches against the above three efficient FDI attacks on DSE. Compared to existing approaches, our proposed approaches have three major differences and significant strengths: (1) they defend against the three FDI attacks on dynamic power state estimation rather than static power state estimation, (2) they give a robust estimator that can accurately extract a subset of attack-free sensors for power state estimation, and (3) they adopt the little-known Mahalanobis distance in the consistency check of power sensor measurements, which is different from the Euclidean distance used in all the existing studies on power state estimation. We mathematically prove that the Mahalanobis distance is not only useful but also much better than the Euclidean distance in the consistency check of power sensor measurements. Our time complexity analysis shows that the proposed two robust defense approaches are efficient. Moreover, to demonstrate the effectiveness of the proposed approaches, we compare them with the three well-known approaches: the least square approach, the Imhotep-SMT approach, and the MEE-UKF approach. Experiments show that the proposed approaches further reduce the estimation error by two orders of magnitude and four orders of magnitude compared to the Imhotep-SMT approach and the least square approach, respectively. Whereas our approach has a similar root mean square error as the Imhotep-SMT approach has, our approach provides a more stable result.