# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1702.00884