  Cubature Kalman Filter (CKF) has good performance when handling nonlinear
dynamic state estimations. However, it cannot work well in non-Gaussian noise
and bad data environment due to the lack of auto-adaptive ability to measure
noise statistics on line. In order to address the problem of behavioral decline
and divergence when measure noise statistics deviate prior noise statistics, a
new robust CKF (RCKF) algorithm is developed by combining the Huber's
M-estimation theory with the classical CKF, and thereby it is proposed to
coping with the dynamic state estimation of synchronous generators in this
study. The simulation results on the IEEE-9 bus system and New England
16-machine-68-bus system demonstrate that the estimation accuracy and
convergence of the proposed RCKF are superior to those of the classical CKF
under complex measurement noise environments including different measurement
noises and bad data, and that the RCKF is capable of effectively eliminating
the impact of bad data on the estimation effects.
