STEKF协同残差归一化的感应电机转速辨识方法

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摘要

为了提高感应电机无速度传感器矢量控制系统的性能,提出了一种强跟踪扩展卡尔曼滤波(STEKF)协同残差归一化(NR)的自适应转速估计方法。通过将渐消因子引入到状态预测协方差矩阵中,实时在线调整增益矩阵,强迫输出残差序列保持相互正交。同时,通过对残差进行归一化处理,自适应地调节渐消因子,消除由残差本身数值差异引起的信息不对称。该文提出的方法提高了系统模型对于实际系统以及外部环境变化的适应性,有效降低了估计误差,满足对于低速的估计要求。对基于STEKF协同残差归一化观测器的感应电机无速度传感器矢量控制系统进行了实验验证,实验结果证明了算法的正确性和有效性。

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	尹忠刚
	李国银
	张延庆
	孙向东
	钟彦儒

关键词 ：强跟踪扩展卡尔曼滤波, 残差归一化, 自适应转速估计, 渐消因子

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

收稿日期: 2016-04-18 出版日期: 2017-03-22

PACS:

TM343

基金资助:

国家自然科学基金项目(51307139)、陕西省青年科技新星资助项目(2015KJXX-29)和陕西省工业攻关项目(2014K08-30)资助。

通讯作者: 尹忠刚男,1982年生,博士,副教授,研究方向为高性能交流调速系统与电力电子变换器数字化控制。E-mail:smart860@163.com

作者简介: 李国银男,1992年生,硕士研究生,研究方向为高性能变频调速系统控制策略。E-mail:liguoyin199@163.com

引用本文:

尹忠刚,李国银,张延庆,孙向东,钟彦儒. STEKF协同残差归一化的感应电机转速辨识方法[J]. 电工技术学报, 2017, 32(5): 86-96. Yin Zhonggang,Li Guoyin,Zhang Yanqing,Sun Xiangdong,Zhong Yanru. A Speed Estimation Method Based on Strong Tracking Extended Kalman Filter with Normalized Residuals for Induction Motors. Transactions of China Electrotechnical Society, 2017, 32(5): 86-96.

链接本文:

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

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