Feasibility studies of applying Kalman Filter techniques to power system dynamic state estimation

Abstract

The lack of dynamic information in the operation of power systems can be attributed to the use of steady state estimators, which generate the input values for many operational tools. This paper investigates the feasibility of applying Kalman Filtering techniques to include dynamic state variables in the state estimation process. The proposed Kalman Filter based dynamic state estimation is tested on a multi-machine system with both large and small disturbances. Sensitivity studies of the dynamic state estimation performance with respect to sampling rate and noise level are presented as well. The study results show that there is a promising path forward for the implementation of Kalman Filter based dynamic state estimation in conjunction with the emerging phasor measurement technologies.