Dynamic state estimation in power systems using Kalman filters

Dynamic state estimation of power systems is necessary for wide area control purposes. Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper two popular nonlinear estimation approaches: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to estimate main states of a simple power system using high rate data provided by Phasor Measurement Unit (PMU). A case study using a simple power system model is presented to illustrate the effectiveness of proposed approaches.