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

arXiv: https://arxiv.org/abs/1702.00884 · HTML: https://mlsanity.com/paper/1702.00884/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2017.

## Built on by (most cited first, 17 of 17)

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- [Selection-Induced Contraction of Innovation Statistics in Gated Kalman Filters](https://mlsanity.com/paper/2512.18508/) (2025)
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- [Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators](https://mlsanity.com/paper/2606.14195/) (2026)
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## Abstract (arXiv)

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.