Quantum mechanics

Algorithm

Gaussian

Control (management)

Discretization

Statistics

Extended Kalman filter

Gaussian noise

Computer science

Mathematical optimization

Fast Kalman filter

Outlier

Physics

Mathematics

Image (mathematics)

Mathematical analysis

Artificial intelligence

Kalman filter

Noise measurement

Control theory (sociology)

Noise reduction

Noise (video)

Robust statistics

IEEE Access

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.

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