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Cubature Kalman Filter (CKF) has good performance when handling nonlineardynamic state estimations. However, it cannot work well in non-Gaussian noiseand bad data environment due to the lack of auto-adaptive ability to measurenoise statistics on line. In order to address the problem of behavioral declineand divergence when measure noise statistics deviate prior noise statistics, anew robust CKF (RCKF) algorithm is developed by combining the Huber'sM-estimation theory with the classical CKF, and thereby it is proposed tocoping with the dynamic state estimation of synchronous generators in thisstudy. The simulation results on the IEEE-9 bus system and New England16-machine-68-bus system demonstrate that the estimation accuracy andconvergence of the proposed RCKF are superior to those of the classical CKFunder complex measurement noise environments including different measurementnoises and bad data, and that the RCKF is capable of effectively eliminatingthe impact of bad data on the estimation effects.

Dynamic State Estimation of Synchronous Machines Using Robust Cubature Kalman Filter Against Complex Measurement Noise Statistics