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A Deviation-based Detection Method against False Data Injection Attacks in Smart Grid
2021
IEEE Access
State estimation plays a vital role to ensure safe and reliable operations in smart grid. Intelligent attackers can carefully design a destructive and stealthy false data injection attack (FDIA) sequence such that commonly used weighted least squares estimator combined with residual-based detection method is vulnerable to the FDIA. To effectively defend against an FDIA, in this paper, we propose a robust deviationbased detection method, in which an additional Kalman filter is introduced while
doi:10.1109/access.2021.3051155
fatcat:aklirnzj4vctfkgvswswonatfi