复杂噪声条件下基于抗差容积卡尔曼滤波的发电机动态状态估计

doi:10.19595/j.cnki.1000-6753.tces.181150

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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.

Key words： Dynamic state estimation synchronous machines cubature Kalman filter M-estimation theory measure noise statistics bad data phasor measurement units data

Received: 04 July 2018 Published: 20 September 2019

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	Li Yang
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	Chen Liang
	Li Guoqing

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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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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.181150 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I17/3651

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