基于遗传算法优化的定子磁链扩展卡尔曼估计方法

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (603 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The performance of direct torque control (DTC) for induction motor is depended on the stator flux estimation in a large degree. Extended Kalman filter (EKF) is useful to estimate state variables when the measured signal is mixed with noise. A flux linkage flux estimation method for induction motor based on EKF theory is presented in this paper, where, the stator current, stator resistance as well as linkage flux are regarded as the state variables to be estimated by joint filtering. For the sake of improving the accuracy of the filtering, genetic algorithm (GA) is introduced to optimize the noise matrix in EKF. The closed loop system composed by DTC controller and the proposed linkage flux observer with optimized filtering parameter has better estimating accuracy and dynamic low speed performance than ordinary EKF-DTC scheme. The effectiveness is verified by simulation and experimental results.

Key words： Flux estimation extended Kalman filter DTC genetic algorithm

Received: 21 January 2008 Published: 17 February 2014

PACS:

TM461

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhang Yongjun
	Wang Jing
	Li Huade

Cite this article:

Zhang Yongjun,Wang Jing,Li Huade. A Method of the Stator Flux EKF Estimation for Induction Motors Based on Genetic Algorithm Optimizing[J]. Transactions of China Electrotechnical Society, 2009, 24(9): 64-70.

URL:

http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2009/V24/I9/64

[1] Deluca A, Ulivi G. Design of exact nonlinear controller for induction motors [J]. IEEE Trans. Automat. Contr., 1989, 34(12): 1304-1307.
[2] Jeon S H, Oh K K, Choi J Y. Flux observer with online tuning of stator and rotor resistances for induction motors[J]. IEEE Trans. on Industrial Electronics, 2002, 49(3):653-664.
[3] Karanayil B, Rahman M F, Grantham C. Stator and rotor resistance observers for induction motor drive using fuzzy logic and artificial neural networks[J]. IEEE Trans. on Energy Conversion, 2005, 20(4):771-780.
[4] Wang K, Chiasson J, Bodson M, et al. A nonlinear least squares approach for identification of the induction motor parameters [J]. IEEE Trans. Automat. Contr., 2005, 50(10). 1622-1628.
[5] Shyu Kuokai, Shang Lijen, Chen Hwangzhi. Flux compensated direct torque control of induction motor drives for low speed operation [J]. IEEE Trans. on Power Electronics, 2004, 19(6):1608-1613.
[6] Nik Rumzi Nik Idris, Adbul Halim Mohamed Yatim. An improved stator flux estimation in steady-state operation for direct torque control of induction machines[J]. IEEE Trans. on Industry Application, 2002, 38(1):110-116.
[7] Hu Jun, Wu Bin. New integration algorithms for estimating motor flux over a wide speed range [J]. IEEE Trans. on Power Electronics, 1998, 13(5): 969-977.
[8] MA X M. Extended Kalman filter for speed sensor-less DTC based on DSP[C]. Proc. of the 4th World Congress on Intelligent Control and Automation, 2002: ll9-122．
[9] Dend Z L, Wang J G．Adaptive extended Kalman filtering for nonlinear systems [J]．Preprints of the 17th JAACE Symposium on Stochastic Theory and its Applications, 1987, l3(5)：375-379．
[10] 邓自立．自校正滤波理论及其应用[M]．哈尔滨：哈尔滨工业大学出版社, 2003.
[11] 徐景硕, 秦永元, 彭蓉．自适应卡尔曼滤波器渐消因子选取方法研究[J]．系统工程与电子技术, 2004, 26(11)：1152-1154．
Xu Jingshuo, Qin Yongyuan, Peng Rong. New method for selecting adaptive Kalman filter [J]. Systems Engineering and Electronics, 2004, 26(11): 1152-1154．
[12] Shi K L, Chan T F, Wong Y K. Speed estimation of an induction motor drive using an optimized extended Kalman filter[J]. IEEE Trans. on Industrial Electronics, 2002, 149(1):124-133.
[13] Chen Junjie, Lu Jun, Huang Weiyi. Non-linearity rectification of sensor systems based on genetic neural networks[J]. Chinese Journal of Scientific Instrument, 2003, 24(2): 201-204.