Dynamic state estimation for synchronous machines based on interpolation H∞ extended Kalman filter

Abstract

Dynamic state estimation is essential for monitoring and analyzing power system stability. With high sampling rates and well synchronized data, phasor measurement unit (PMU) has been widely used in dynamic state estimation (DSE). However, the PMU data cannot be used directly by controlling and scheduling due to the stochastic noise. Based on interpolation H∞ extended Kalman filter (IHEKF), in this paper, a novel dynamic state estimation for synchronous machines is proposed. On the basis of the extended Kalman filter (EKF), the proposed method uses the adaptive interpolation method and the H∞ theory to improve the accuracy of estimation and the robustness about measurement noise. Finally, simulation shows that the IHEKF performs well in the estimation accuracy, as well as the robustness about measurement noise, compared with the EKF.