Adaptive Two-Stage Unscented Kalman Filter for Dynamic State Estimation of Synchronous Generator Under Cyber Attacks Against Measurements

This paper develops an adaptive two-stage unscented Kalman filter (ATSUKF) to accurately track operation states of the synchronous generator (SG) under cyber attacks. To achieve high fidelity, considering the excitation system of SGs, a detailed 9(th)-order SG model for dynamic state estimation is established. Then, for several common cyber attacks against measurements, a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in parallel. Subsequently, to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions, a multi-dimensional adaptive factor matrix is derived to modify the noise covariance matrix. Finally, a large number of simulation experiments are carried out on the IEEE 39-bus system, which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.