Neural Control of the Induction Motor Drive: Robust Against Rotor and Stator Resistances Variations and Suitable for Very Low and High Speeds
Subject Areas : electrical and computer engineeringH. Moayedi Rad 1 * , M. A. Shamsi-Nejad 2 , mohsen Farshad 3
1 -
2 -
3 -
Keywords: Artificial neural network (ANN) induction motor drives robust vector control very low speed,
Abstract :
In this paper, induction motor speed control drive is designed with application two multilayer feed-forward neural networks. That those are used one for generate PWM pulse and other for estimation of required torque and flux information. For trained of the PWM wave generate neural network is used from compound information two voltage and current classic model. Also, against general classic models for generate of the switching pulses is used as compound from reference voltage and current two motor phases. With these ideas are eliminated problems of the voltage and current classic models (flux saturation in current model for high speeds and voltage drop in voltage model for low speeds). As voltage profile is improved in this paper. The required feedback signals estimation (including: rotor flux, torque, etc.) is estimated by multilayer feed-forward neural network. That for robustness of the above estimator against rotor and stator resistances variations in time work of motor is used from compound trained data of the voltage and current classic models, because the voltage and current of the general classic models to sequence are independent of rotor and stator resistances. The simulation results by MATLAB-Simulink verify the proposed drive in improvement of the speed profile in transient and steady-state operating modes. Also, it verify clearly robust of the proposed drive against rotor and stator resistances variations in time work.
[1] B. K. Bose, Modern Power Electronics and AC Drives, Upper Saddle River, NJ: Prentice - Hall, 2002.
[2] F. Blaschke, "The principle of field orientation as applied to the new transvector closed - loop control system for rotating - field machines," Siemens Rev, vol. 39, no. 3, pp. 217-220, May 1972.
[3] P. Vas, The Control of AC Machines, Oxford Univ., 1990.
[4] J. N. Nash, "Direct torque control, induction motor vector control without an encoder," IEEE Trans. on Industrial Applications, vol. 33, no. 2, pp. 333-341, Mar./Apr. 1997.
[5] D. Casadei, G. Serra, and A. Tani, "Implementation of a direct torque control algorithm for induction motor based on discrete space vector modulation," IEEE Trans. on Power Electronics, vol. 15, no. 4, pp. 769-777, Jul. 2000.
[6] S. Mir, M. E. Elbuluk, and D. S. Zinger, "PI and fuzzy estimators for tuning the stator resistance in direct torque control of induction machines," IEEE Trans. on Power Electronics, vol. 13, no. 2, pp. 279-287, Mar. 1998.
[7] M. G. Simoesand and B. K. Bose, "Neural network based estimation of feedback signals for a vector controlled induction motor drive," IEEE Trans. on Industrial Applications, vol. 31, no. 3, pp. 620-629, May/Jun. 1995.
[8] B. K. Bose, "Neural network applications in power electronics and motor drives - an introduction and perspective," IEEE Trans. on Industrial Electronics, vol. 54, no. 1, pp. 14-33, Feb. 2007.
[9] R. D. Lorenz and D. B. Lawson, "A simplified approach to continuous on - line tuning of field oriented induction motor drives," IEEE Trans. on Industrial Applications, vol. 26, no. 3, pp. 420-424, May/Jun. 1990.
[10] M. P. Kazmierkowski, "A novel vector control scheme for transistor PWM inverter - fed induction motor drive," IEEE Trans. on Industrial Electronics, vol. 38, no. 1, pp. 41-47, Feb. 1991.
[11] L. C. Zai and T. A. Lipo, "An extended kalman filter approach in rotor time constant measurement in PWM induction motor drives," in Proc. Conf. Rec. IEEE - IAS Annual Meeting, vol. 1, pp. 177-183, Feb. 1987.
[12] P. J. Chrzan and H. Klaassen, "Parameter identification of vector - controlled induction machines," Electrical Engineering, vol. 79, no. 1, pp. 39-49, May/Jun. 1996.
[13] J. Faiz and M. B. B. Sharifian, "Different techniques for real time estimation of an induction motor rotor resistance sensorless DTC for EV," IEEE Trans. on Energy Conversion, vol. 16, no. 1, pp. 104-109, May 2001.
[14] T. G. Habetler, F. Profumo, M. Pastorelli, and L. M. Tolbert, "Direct torque control of induction machines using space vector modulation," IEEE Trans. on Industrial Applications, vol. 28, no. 5, pp. 1045-1053, Sep./Oct. 1992.
[15] I. Takahashi and Y. Ohmori, "High - performance direct torque control of an induction motor," IEEE Trans. on Industrial Applications, vol. 25, no. 2, pp. 257-264, Mar./Apr. 1989.
[16] 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 drive," IEEE Trans. on Industrial Electronics, vol. 54, no. 1, pp. 167-176, Feb. 2007.
[17] H. M. Kojabadi, L. Chang, and R. Doraiswami, "A MRAS - based adaptive pseudoreduced - order flux observer for sensorless induction motor drives," IEEE Trans. on Power Electronics, vol. 20, no. 4, pp. 930-176, Jul. 2005.
[18] K. K. Shyu, L. J. Shang, H. Z. Chen, and K. W. Jwo, "Flux compensated direct torque control of induction motor drives for low speed operation," IEEE Trans. on Power Electronics, vol. 19, no. 6, pp. 1608-1613, Nov. 2004.
[19] F. Lin, H. Shieh, K. Shyu, and P. Huang, "Online gain tuning PI controller using realcoded genetic algorithm," Electric Power Systems Research, vol. 72, no. 2, pp. 157-169, Dec. 2004.
[20] F. Lin, W. Chou, and P. Huang, "Adaptive sliding mode controller based on real time genetic algorithm for induction motor servo drive," in IEE Proc. Electric Power Applications, vol. 150, no. 1, pp. 1-13, Apr. 2003.
[21] S. M. Gadoue, D. Giaouris, and J. W. Finch, "Artificial intelligence - based speed control of DTC induction motor drives—A comparative study," Electric Power Systems Research, vol. 79, no. 1, pp. 210-219, Jan. 2009.
[22] A. Dey, B. Singh, B. Dwivedi, and D. Chandra, "Vector control of three - phase induction motor using artificial intelligent technique," ARPN J. of Engineering and Applied Sciences, vol. 4, no. 4, pp. 57-67, Jun. 2009.
[23] D. Casadei, G. Serra, A. Stefani, A. Tani, and L. Zarri, "DTC drives for wide speed range applications using a robust flux - weakening algorithm," IEEE Trans. on Industrial Electronics, vol. 54, no. 9, pp. 2451-2461, Oct. 2007.
[24] K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, vol. 2, no. 4, pp. 303-314, Jul. 1989.
[25] D. E. Rummelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in D. E. Rummelhart and J. L. McClelland, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, Chapter 8, pp. 318-362, MIT Press, Cambridge, MA., 1986.