Cubature Kalman Filter as a Robust State Estimator Against Model Uncertainty and Cyber Attacks in Power Systems (2503.21070v1)

Abstract: It is known that the conventional estimators such as extended Kalman filter (EKF) and unscented Kalman filter (UKF) may provide favorable performance; However, they may not guarantee the robustness against model uncertainty and cyber attacks. In this paper, we compare the performance of cubature Kalman filter (CKF) to the conventional nonlinear estimator, the EKF, under the affect of model uncertainty and cyber-attack. We show that the CKF has better estimation accuracy than the EKF under some conditions. In order to verify our claim, we have tested the performance various nonlinear estimators on the single machine infinite-bus (SMIB) system under different scenarios. We show that (1) the CKF provides better estimation results than the EKF; (2) the CKF is able to detect different types of cyber attacks reliably which is superior to the EKF.