Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation

TLDR

Accurate estimation of synchronous machine dynamic states, such as rotor angle and speed, is essential for monitoring transient stability, and the covariance matrices of process and measurement noise (Q and R) critically affect Kalman filter performance, yet conventional ad‑hoc methods are inadequate. The study proposes an adaptive filtering approach to estimate process and measurement noise covariances Q and R for improved dynamic state estimation of synchronous machines. The method adapts Q and R based on innovation and residual signals within the extended Kalman filter framework. Simulations on a two‑area model show that the proposed adaptive estimation is more robust to initial Q and R errors than conventional methods when estimating synchronous machine dynamic states.

Abstract

Accurate estimation of the dynamic states of a synchronous machine (e.g., rotor's angle and speed) is essential in monitoring and controlling transient stability of a power system. It is well known that the covariance matrixes of process noise (Q) and measurement noise (R) have a significant impact on the Kalman filter's performance in estimating dynamic states. The conventional ad-hoc approaches for estimating the covariance matrixes are not adequate in achieving the best filtering performance. To address this problem, this paper proposes an adaptive filtering approach to adaptively estimate Q and R based on innovation and residual to improve the dynamic state estimation accuracy of the extended Kalman filter (EKF). It is shown through the simulation on the two-area model that the proposed estimation method is more robust against the initial errors in Q and R than the conventional method in estimating the dynamic states of a synchronous machine.