Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines under Unknown Measurement Noise Statistics
For power system operators, this method improves state estimation reliability under realistic noise conditions, but it is an incremental improvement over existing robust filtering techniques.
The paper proposes a robust cubature Kalman filter (RCKF) that integrates Huber's M-estimation to handle non-Gaussian measurement noise and outliers in dynamic state estimation of synchronous machines. Simulations on two power systems show RCKF outperforms classical CKF in tracking accuracy and robustness.
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.