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Kalman-type filtering techniques including cubature Kalman filter (CKF) doesnot work well in non-Gaussian environments, especially in the presence ofoutliers. To solve this problem, Huber's M-estimation based robust CKF (RCKF)is proposed for synchronous machines by combining the Huber's M-estimationtheory with the classical CKF, which is capable of coping with thedeterioration in performance and discretization of tracking curves whenmeasurement noise statistics deviatefrom the prior noise statistics. Theproposed RCKF algorithm has good adaptability to unknown measurement noisestatistics characteristics including non-Gaussian measurement noise andoutliers. The simulation results on the WSCC 3-machine 9-bus system and NewEngland 16-machine 68-bus system verify the effectiveness of the proposedmethod and its advantage over the classical CKF.

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