Applications of strong tracking filter in power system dynamic state estimation

In this paper, a new method of strong tracking filter (STF) for power system dynamic state estimation is proposed. In the new method, time-varying suboptimal fading factor is introduced in extended Kalman filter (EKF), so that the state prediction error covariance matrix and the corresponding gain matrix is on-line rectified. Consequently, the state estimation residual variance is least, at the same time, the residual sequences are orthogonal to each other, which offset the EKF´s defects, such as bad robustness caused by model uncertainties, unsafe estimation results or filter divergence, ect. At the end of the paper, simulation results show that the presented method has excellent forecasting and filtering performance under abnormal circumstances, such as bad data, sudden load change and network topology error conditions.