Dynamic State Estimation of Smart Grid Based on CKF under False Data Injection Attacks

The accurately estimated state is of great importance to a stable running condition of smart grid. The emergence of cyber attacks brings new challenges to the state estimation of smart grid. Especially, false data injection (FDI) attacks deteriorate the accuracy of state estimation by injecting the false data into the measurement device. To solve the problem, a cubature Kalman filter (CKF) is proposed, which can estimate the dynamic state of smart grid under FDI attacks. Firstly, due to the complexity of the state equation of smart grid, this paper adopts Holt's two-parameter exponential smoothing method to establish the state equation. Secondly, according to the principle that the measurement residuals before and after the FDI attack are equal, the expressions of the attack vectors are established. And they are applied to the measurement quantities to avoid the conventional bad data detection. Then, the cubature Kalman filter algorithm is utilized to estimate the dynamic state of the smart grid attacked by FDI. Finally, the simulated results verify effectiveness of the proposed method.