Adaptive Adjustment of Noise Covariance in Kalman Filter for Dynamic State Estimation

TL;DR

This paper introduces an adaptive Kalman filtering method that dynamically estimates noise covariance matrices to enhance the accuracy of synchronous machine state estimation in power systems.

Contribution

It proposes a novel adaptive approach to estimate process and measurement noise covariances using innovation and residuals, improving EKF performance.

Findings

More robust against initial errors in noise covariance estimates

Improved accuracy in dynamic state estimation of synchronous machines

Demonstrated effectiveness through simulation on a two-area power system

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.