Power System Dynamic State Estimation Based on a New Particle Filter

Abstract

Abstract In order to improve the performance of power system dynamic state estimation, a new particle filter for nonlinear filtering problems (Mixed Kalman Particle Filter, MKPF) is introduced. The MKPF method which based on the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), can obtain a more accurate approximate expression of the true distribution. Combined with the real-time data of mixed measurement (WAMS/SCADA), a simulation of power system dynamic state estimation is established. Finally, the simulation results show that the method can quickly follow to the real value after the power system is disturbed and obtain higher estimated accuracy and robustness than the EKF and UKF methods.