Power System Dynamic State Estimation Algorithm Based On Kalman Filter

Power system state estimation is not only the foundation of power system dispatch, control and security assessment, but also the core of Energy Management System. Through state prediction, dynamic state estimation is able to finish the security assessment in order to achieve economic dispatch, prevention control and other online functions. Therefore researches on power system dynamic state estimation based on extended Kalman filter were carried outAlong with the derivation of Kalman filter algorithm, the operating principle of extended Kalman filtering dynamic estimator in power system and the problems facing practical application were detailed. Then several improved algorithms were proposed.On the basis of extended Kalman filter algorithm, a new dynamic state estimation algorithm based on adaptive Kalman filter was brought up. During the process of state estimation, recursive extended least square was introduced to update the parameters of state model, at the same time exponential weighted algorithm was put in use to estimate the statistical characteristic of time-varying noise. Thus the errors of state estimation could be reduced. What's more, cubic spline interpolation was proposed to fit the state function using historical state data. After that, the initial value of state transfer matrix could be estimated. All of that improvements together with sparse matrix technique contribute to the cut of calculation period and the increase of accuracy.The simulation results show that with proposed algorithm, better prediction and estimate results could be got under normal condition in power system, further more when sudden changes or bad data were involved, this algorithm could still work with nice adaptability.