STEKF协同残差归一化的感应电机转速辨识方法

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Abstract

To improve the performance of sensorless induction motor(IM)drives,an adaptive speed estimation method based on the strong tracking extended Kalman filter with normalized residuals algorithm(NR-STEKF)for induction motors is proposed in this paper.With this method,the fading factor is introduced into the covariance matrix of the predicted state,which forces the residual sequences orthogonal to each other and tunes the gain matrix online.Simultaneously,the fading factor can be continuously self-tuned with the normalized residuals algorithm,and the information asymmetry which is caused by the residual numerical difference is eliminated.Therefore,the proposed method improves the model adaptability to the actual systems and the environmental variations,reduces the speed estimation error,and can satisfy the estimation request running at low speed.The correctness and the effectiveness of the proposed method are verified by the experimental results.

Key words： Strong tracking extended Kalman filter(STEKF) normalized residuals(NR) adaptive speed estimation fading factor

Received: 18 April 2016 Published: 22 March 2017

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TM343

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	Yin Zhonggang
	Li Guoyin
	Zhang Yanqing
	Sun Xiangdong
	Zhong Yanru

Cite this article:

Yin Zhonggang,Li Guoyin,Zhang Yanqing等. A Speed Estimation Method Based on Strong Tracking Extended Kalman Filter with Normalized Residuals for Induction Motors[J]. Transactions of China Electrotechnical Society, 2017, 32(5): 86-96.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I5/86

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