Research On Dynamic State Estimation Of Power System Based On Kalman Filter

Power system state estimation is an integral part of energy management systems(EMS).The data used by the power system dispatch center mainly comes from the data acquisition and monitoring system(SCADA)and the phasor measurement unit(PMU).The state estimation of the power system uses the redundancy of the measurement information to estimate the voltage amplitude and phase angle in the power system,identify and eliminate errors caused by random interference,thereby obtaining an estimate of the system operating state,and achieving the current operating state of the system Safety monitoring plays an important role in modern power systems.Extended Kalman Filter(EKF)and Unscented Kalman Filter(UKF)are widely used methods in dynamic state estimation of power systems.In this paper,two methods are studied and analyzed,and an adaptive extended Kalman filter method and a robust unscented Kalman filter method are proposed.The main work of this article is as follows:1.Introduce the structure and data measurement method of SCAD A system to PMU system,analyze and compare the two,and point out the source of data error in state estimation.2.Adaptive extended Kalman filtering algorithm.Basic idea:In order to effectively solve the problem that the filtering effect is reduced due to inaccurate prediction estimates due to historical data or excessive measurement of filtered historical values,start with the EKF prediction module,introduce adaptive filtering,and make the two parameter index The smoothing method can obtain better smoothing results and improve the estimation ability of the prediction modulec On MATLAB/Simulink platform,the algorithm is simulated and tested on an actual 85-node power system.The experimental results show that this method has better performance than EKF.3.Robust unscented Kalman filtering algorithm.Basic idea:Before using UKF for filtering,use the bad data identification method based on the operating mode,identify bad data based on the change law of the load,and generate a pseudo quantity measurement instead.The advantage is that the filtering result does not receive the residual correlation effect,and avoids the phenomenon of residual flooding or residual pollution.Then on MATLAB/Simulink platform,the algorithm was simulated on IEEE-33 node system and an actual 107-node system.The experimental results confirmed that the algorithm has better estimated performance compared with UKF in the presence of bad data.Has higher stability and robustness.