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Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive

by C. Kamal Basha, M. Suryakalavathi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Number 6
Year of Publication: 2013
Authors: C. Kamal Basha, M. Suryakalavathi
10.5120/13742-1423

C. Kamal Basha, M. Suryakalavathi . Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive. International Journal of Computer Applications. 79, 6 ( October 2013), 1-6. DOI=10.5120/13742-1423

@article{ 10.5120/13742-1423,
author = { C. Kamal Basha, M. Suryakalavathi },
title = { Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 79 },
number = { 6 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number6/13742-1423/ },
doi = { 10.5120/13742-1423 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:52:16.661517+05:30
%A C. Kamal Basha
%A M. Suryakalavathi
%T Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 6
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In speed sensorless controlled induction motor drive, the rotor flux based Model Reference Adaptive System (MRAS) for is more popular. In MRAS, the voltage model equations are used in the reference model, which leads to poor performance of the drive at low speeds. In this paper, a new stator current based MRAS is presented. In this measured stator currents of induction motor are used as a reference model to avoid the use of a pure integrator. Estimated stator currents are used as an adjustable model. The estimation of stator currents requires the information of the rotor flux which can be obtained from the voltage or current model. This encounters instability and dc drift. To overcome these problems, flux observer is proposed based on Neural Network (NN). This offline trained multilayer feed-forward NN flux observer applied to indirect vector controlled speed sensorless induction motor drive has been verified by MATLAB/SIMULINK.

References
  1. Rajasekara. K, Kawamura. A, K. Matsuse, "Sensorless Control of AC MotorDrives: Speed and Position Sensorless Operation", IEEE Press, Piscataway, NJ, 1996.
  2. L. Ben Brahim, S. Tadakuma, and A. Akdag, "Speed control of induction motor without rotational transducers," IEEE Trans. Ind. Appl. , vol. 35, no. 4, pp. 844–850, Jul. /Aug. 1999.
  3. Hisao Kubota, KoukiMastuse and Takayoshi Nakano, "DSP based speed adaptive flux observer of induction motor", IEEE Trans. on Industry Applications, Vol. 29,No. 2, 1993, pp344-348.
  4. Abbou, A. ; Mahmoudi, H. "Implementation of a Sensorless Speed Control of Induction Motor Using RFOC Strategy" International Review of Electrical Engineering(IREE), pp-730-737, JUL-AUG 2008.
  5. E. Levi and M. ang, "A speed estimator for high performance sensorless control of induction motor in the field weakening region" IEEE Trans. Power Electronics, vol. 17,no. 3,pp. 365-378, May 2002.
  6. Y. R. Kim,K. S. Sul and M. H. Park, "Speedsensorless vector control of induction motor using extended Kalman filter" IEEE Trans. Ind. Appl. , vol. 30, no. 5,pp1225-1233,Sep/Oct. 1994.
  7. Zhang Wei, Cai Wei Sheng "Flux Observer for Field Oriented Induction Motors based on EKF" 2010 2nd International Conference on Software Technology and Engineering(ICSTE) pp v2-240-v2-243.
  8. M. BenHamed and L. Sbita, "Speed Sensorless Indirect Stator Field Oriented Control of Induction Motor based on Luenberger observer", IEEE-ISIE06-2006.
  9. Mezouar, A. ; Fellah, M. K. ; Hadjeri, S. "Speed sensorless vector control of induction motors using singularly perturbed sliding mode observer" International Review of Electrical Engineering(IREE), pp398 -405, MAY-JUN 2007.
  10. . K. Gherram, K. Yazid and M. Menaa "Sensorless Indirect Vector Control of an Induction Motor by ANNs Observer and EKF" 18th Mediterranean Conference on Control & Automation Congress Palace Hotel, Marrakech, Morocco June 23-25, 2010, pp 521-526.
  11. M. M. Krishan, "Sliding Mode Control with MRAC Technique Applied to an Induction Motor Drives" International Review of Automatic Control (IREACO) Vol. 1. n. 1, pp. 42-48, May 2008.
  12. Hu Jun B. R. uggal and M. V. ilathgamuwa, "A MRAS based speed sensorless field oriented control of induction motor with on line stator resistance tuning" IEEE Trans. Ind. Appl. 1998.
  13. S. Tami, H. Sugimoto and Y. Masao "Speed sensorless vector control of induction motor with model reference adaptive system" in conf. IEEE/IAS. Annu. Meeting, 1987,pp. 189-195.
  14. M. Rashed and A. F. Stronach, "A stable back-EMF MRAS based sensorless low speed inaction motor drive insensitive to stator resistance variation," Proc. Inst. Elect. Eng Elect. Power Appl. , vol. 151,no6, pp 1685-693, nov. 2004.
  15. T. Orlowska-Kowaska and M. ybkowski, " Novel MRAS type rotor speed and flux estimator for speed sensorless induction motor drive,"Elct. Rev. (polond), vol. 82, no. 11, pp35-38,2006.
  16. M. Mostefai, A. Bendiabdellah "A MRAS-based Speed Sensorless Vector Control of Induction Motor with Rotor-Inverse Time Constant Adaptation" International Review of Automatic Control (IREACO), Vol. 1, pp. 49-56, May 2008
  17. S. K. Mondal, J. O. P. Pinto, and B. K. Bose, "A neural network based space-vector PWM controller for a three voltage-fed inverter induction motor drive," IEEE Trans. Ind. Appl. , vol. 38, no. 3, pp. 660–669, May 2002.
  18. B. Burton, R. G. Harley, G. Diana, and J. L. Rodgerson, "Implementation of a neural network to adaptively identify and control VSI-fed induction motor stator currents," IEEE Trans. Ind. Appl. , vol. 34, no. 3, pp. 580–588, May-Jun. 1998.
  19. L. Ben-Brahim, S. Tadakuma, and A. Akdag, "Speed control of induction motor without rotational transducers," IEEE Trans. Ind. Appl. , vol. 35, no. 4, pp. 844–850, Jul. /Aug. 1999.
  20. . B. Karanayil, M. F. Rahman, and C. Grantham, "Online Stator and rotor resistance estimation scheme using artificial neural networks for vector controlled speed sensorless induction motor drives," IEEE Transactions on Industrial Electronics, vol. 54, pp. 167-176, 2007.
  21. L. M. Grzesiak and B. Ufnalski, "Neural stator flux estimator with dynamical signal preprocessing," in Proc. IEEE AFRICON, 2004.
  22. A. Ba-Razzouk, A. Cheriti, G. Olivier, and P. Sicard, "Field oriented control of Induction Motors using Neural Network decouplers," IEEE Transactions on Power Electronics, vol. 12, pp. 752-763, 1997.
  23. K. S. Narendra and Parthasarathy, K. "Identification and control of dynamical systems using neural networks". In IEEE Trans. Neural Network. , vol. 1, no. 1, pp. 4–27. 1990.
  24. Shady M. Gadoue, Member, IEEE, Damian Giaouris, Member, IEEE, and John W. Finch, Senior Member, IEEE"Sensorless Control of Induction Motor Drives at Very Low and Zero Speeds Using Neural Network Flux Observers" IEEE Transactions on Industrial Electronics vol. 56, no. 8, , pp,3029-3039 August 2009
  25. C. KamalBasha and M. Suryakalavathi "Speed sensorless vector control of Induction motor using Stator current based MRAS Scheme" International Review of Automatic Control (I. RE. A. CO. ), Nov. 2011
Index Terms

Computer Science
Information Sciences

Keywords

Induction Motor Sensorless control Model Reference Adaptive System (MRAS) Neural Network flux observer Vector control.

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