Residual Saturation Based Kalman Filter for Smart Grid State Estimation Under Cyber Attacks

Most of the traditional state estimation algorithms are provided false alarm when there is attack. This paper proposes an attack-resilient algorithm where attack is automatically ignored, and the state estimation process is continuing which acts a grid-eye for monitoring whole power systems. After modeling the smart grid incorporating distributed energy resources, the smart sensors are deployed to gather measurement information where sensors are prone to attacks. Based on the noisy and cyber attack measurement information, the optimal state estimation algorithm is designed. When the attack is happened, the measurement residual error dynamic goes high and it can ignore using proposed saturation function. Moreover, the proposed saturation function is automatically computed in a dynamic way considering residual error and deigned parameters. Combing the aforementioned approaches, the Kalman filter algorithm is modified which is applied to the smart grid state estimation. The simulation results show that the proposed algorithm provides high estimation accuracy.