Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines under Unknown Measurement Noise Statistics

TL;DR

This paper introduces a robust cubature Kalman filter that effectively estimates the state of synchronous machines in environments with unknown, non-Gaussian measurement noise and outliers, improving accuracy and reliability.

Contribution

It combines Huber's M-estimation with the classical CKF to enhance robustness against unknown noise statistics in dynamic state estimation.

Findings

The proposed RCKF outperforms classical CKF in non-Gaussian noise environments.

Simulation results demonstrate improved tracking accuracy and robustness.

The method is effective on large-scale power system models.

Abstract

Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theory with the classical CKF, which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviatefrom the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.