Dynamic State Estimation of Synchronous Machines Based on Robust Cubature Kalman Filter under Complex Measurement Noise Conditions
Li Yang1, Li Jing1, Chen Liang2, Li Guoqing1
1. School of Electrical Engineering Northeast Electric Power University Jilin 132012 China; 2. State Grid Hebei Economic Research Institute Shijiazhuang 050022 China
Abstract 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.
Li Yang,Li Jing,Chen Liang等. Dynamic State Estimation of Synchronous Machines Based on Robust Cubature Kalman Filter under Complex Measurement Noise Conditions[J]. Transactions of China Electrotechnical Society, 2019, 34(17): 3651-3660.
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2019, Vol. 34 
