Robust Dynamic State Estimator to Outliers and Cyber Attacks

To perform power system monitoring and control using synchrophasor measurements, various dynamic state estimators have been proposed in the literature, including the extended Kalman filter (EKF) and the unscented Kalman filter (LIKE). However, they are unable to handle system model parameter errors and any type of outliers, precluding them from being adopted for power system real-time applications. In this paper, we develop a robust iterated extended Kalman filter based on the generalized maximum likelihood approach (termed GM-IEKF) for dynamic state estimation. The proposed GM-IEKF can effectively suppress observation and innovation outliers, which may he induced by model parameter gross errors and cyber attacks. We assess its robustness by carrying out extensive simulations on the IEEE 39-bus test system. From the results, we find that the GM-IEKF is able to cope with at least 25% outliers, including in position of leverage.