Detection of false data injection attacks in smart grid based on adaptive inhibition unscented Kalman filter

Abstract

False data injection attacks (FDIAs) cause incorrect system states by tampering with measurements, seriously affecting the EMS’s control process. However, the well-designed FDIAs can bypass traditional bad data detection (BDD) mechanisms. Aiming at the challenge, we improve the unscented Kalman filter and combine AIUKF with weighted least squares (WLS) to detect FDIAs. Utilizing the different convergence rates of the two estimators, the cosine similarity is introduced for FDIA detection. Various test conditions in IEEE-14-bus are simulated to underline the capability of AIUKF in state estimation. The results indicate that the proposed detection approach is superior for detecting FDIAs.