an effective attack-resilient kalman filter-based approach for dynamic state estimation of synchronous machine

Kalman filtering has been widely considered for dynamic state estimation in smart grids. despite its unique merits, the kalman filter (kf)-based dynamic state estimation can be undesirably influenced by cyber adversarial attacks that can potentially be launched against the communication links in the cyber-physical system (cps). to enhance the security of kf-based state estimation, in this paper, the basic kf-based method is enhanced by incorporating the dynamics of the attack vector into the system state-space model using an observer-based preprocessing stage. the proposed technique not only immunizes the state estimation against cyber-attacks but also effectively handles the issues relevant to the modeling uncertainties and measurement noises/errors. the effectiveness of the proposed approach is demonstrated by detailed mathematical analysis and testing it on two well-known ieee cyber-physical test systems.