基于强跟踪泰勒-卡尔曼滤波器的动态相量估计算法

doi:10.19595/j.cnki.1000-6753.tces.160915

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Abstract The synchronized phasor estimation algorithm is the core of the synchronized phasor measurement technology. So it is very important to improve the measurement accuracy and the dynamic performance of the algorithm in the power system dynamic condition. A dynamic phasor estimation algorithm is proposed in this paper based on strong tracking Taylor-Kalman filter (STKF). First, taken into account the impacts of harmonics and measurement as well as the time-varying characteristics of amplitude or phase, a state space model of dynamic electrical signals is established. Since the Taylor- Kalman filter (TKF) fails to fast track the system parameters mutation when estimating the state variables, the idea of strong tracking filter was introduced, where the estimation covariance matrix can be adjusted adaptively according to the mismatch degree between the theoretical and the actual residual. This change improved the ability of the traditional Kalman filter to track mutation signal. Test results of both numerical signal with noise and fault voltage signal generated by Matlab/Simulink show that the STKF algorithm has better step response performance, measurement accuracy and stability than the TKF algorithm.

Key words： Phasor measurement Kalman filter strong tracking filter dynamic performance

Received: 20 June 2016 Published: 31 January 2018

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TM76

Fund:国家自然科学基金资助项目（51677060）

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	Articles by authors
	Liu Jiebo
	Huang Chun
	Jiang Yaqun
	Tang Tao
	Xie Xing

Cite this article:

Liu Jiebo,Huang Chun,Jiang Yaqun等. Dynamic Phasor Estimator Based on Strong Tracking Taylor-Kalman Filter[J]. Transactions of China Electrotechnical Society, 2018, 33(2): 433-441.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.160915 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I2/433

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