Performances Comparison of Neural Architectures for On-Line Speed Estimation in Sensorless IM Drives
The performance of sensor-less controlled induction
motor drive depends on the accuracy of the estimated speed.
Conventional estimation techniques being mathematically complex
require more execution time resulting in poor dynamic response. The
nonlinear mapping capability and powerful learning algorithms of
neural network provides a promising alternative for on-line speed
estimation. The on-line speed estimator requires the NN model to be
accurate, simpler in design, structurally compact and computationally
less complex to ensure faster execution and effective control in real
time implementation. This in turn to a large extent depends on the
type of Neural Architecture. This paper investigates three types of
neural architectures for on-line speed estimation and their
performance is compared in terms of accuracy, structural
compactness, computational complexity and execution time. The
suitable neural architecture for on-line speed estimation is identified
and the promising results obtained are presented.
[1] K. Hurst, T. Habetler, G. Griva, and F. Profumo, "Speed sensorless
field-oriented control of induction machines using current harmonic
spectral estimation," in Conference Record of Industry Applications
Society Annual Meeting, vol. 1, pp. 601-607, IEEE, Oct. 1994.
[2] A. Ferrah, P. Hogben-Laing, K. Bradley, G. Asher, and M. Woolfson,
"The effect of rotor design on sensorless speed estimation using rotor
slot harmonics by adaptive digital filtering using the maximum
likelihood approach," in Conference Proceedings of the IEEE IAS
Annual Meeting, pp. 128-135, IEEE, Oct. 1997.
[3] M. Rashed and A. F. Stronach, "A stable back-EMF MRAS-based
sensorless low speed induction motor drive insensitive to tator resistance
variation," Proc. Inst. Elect. Eng.ÔÇöElectr. Power Appl., vol. 151, no. 6,
pp. 685-693, Nov. 2004.
[4] P. L. Jansen and R. D. Lorenz, "Transducer less field orientation
concepts employing saturation-induced saliencies in induction
machines," IEEE Transactions on Industry Applications, vol. 32, pp.
1380-1393, Nov/Dec 1996.
[5] M. Schroedl, "Sensorless control of ac machines at low speed and
standstill based on the "inform" method," in Conference Record of
the31st Annual Meeting of the IAS, vol. 1, pp. 270-277, IEEE, Oct.
1996.
[6] S. Maiti, C. Chakraborty, Y. Hori, and M. C. Ta, "Model reference
adaptive controller-based rotor resistance and speed estimation
techniques for vector controlled induction motor drive utilizing reactive
power," IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 594-601, Feb.
2008.
[7] F. Z. Peng and T. Fukao, "Robust speed identification for speed
sensorless vector control of induction machines," IEEE Transactions on
Industry Applications, vol. IA-30, no. 5, pp. 1234-1230, 1994.
[8] K.Narendra and K.Part-ms arathy, "Identification and control of
dynamical system using neural network" IEEE Trans. Neural Networks,
vol.1, no.1, pp.4-27, Mar.1990
[9] Karanayil M. Rahman M.F. and Grantham C. "Online Stator and Rotor
Resistance Estimation Scheme Using Artificial Neural Networks for
Vector Controlled Speed Sensorless Induction Motor Drive" IEEE
Transactions on Industrial Electronics, vol. 54, no. 1, Feb 2007.
[10] S.K.Mondal, J.O.P.Pinto and B.K. Bose, "A neural network based space
vector PWM controller for a three voltage fed inviter induction motor
drive," IEEE Trans.Ind.Appl. vol. 30.no.3.pp 660-669 May 2002.
[11] Maurizio Cirrincione,and Marcello Pucci, "An MRAS-Based Sensorless
High-Performance Induction Motor Drive With a Predictive Adaptive
Model" IEEE Transactions On Industrial Electronics, vol. 52, pp. 532-
551, Apr 2005.
[12] P.Vas, "Sensorless and Direct Torque Control", Oxford University
Press, 1998.
[13] Nicholas K.Treadgold and Tramas D. Gedeon, "Exploring Constructive
Cascade Networks", IEEE Transactions on Neural Networks, vol. 10,
pp.1335-1349, 1999.
[14] K.S.Narendra and K.Parthasarathy, "Gradient Methods for the
Optimization of Dynamical Systems Containing Neural Networks",
IEEE Transactions on Neural Networks, Vol 2, No.2, pp.252-262,
March 1991.
[15] Martin T. Hagan and Mohammad B. Menhaj, "Training Feedforward
Networks with the Marquardt Algorithm", IEEE Transactions on Neural
Networks, Vol 5,No.6, pp.989-993, November 1994.
[16] Martin T.Hagan, Howard B. Demuth and Mark Beale, "Neural Network
Design", Cengage Learning, Pvt.Ltd., India 2008.
[17] Steven W. Smith, "Digital Signal Processing: a Practical Guide for
Engineers and Scientists", Cengage Learning, Pvt.Ltd., India 2008.
[1] K. Hurst, T. Habetler, G. Griva, and F. Profumo, "Speed sensorless
field-oriented control of induction machines using current harmonic
spectral estimation," in Conference Record of Industry Applications
Society Annual Meeting, vol. 1, pp. 601-607, IEEE, Oct. 1994.
[2] A. Ferrah, P. Hogben-Laing, K. Bradley, G. Asher, and M. Woolfson,
"The effect of rotor design on sensorless speed estimation using rotor
slot harmonics by adaptive digital filtering using the maximum
likelihood approach," in Conference Proceedings of the IEEE IAS
Annual Meeting, pp. 128-135, IEEE, Oct. 1997.
[3] M. Rashed and A. F. Stronach, "A stable back-EMF MRAS-based
sensorless low speed induction motor drive insensitive to tator resistance
variation," Proc. Inst. Elect. Eng.ÔÇöElectr. Power Appl., vol. 151, no. 6,
pp. 685-693, Nov. 2004.
[4] P. L. Jansen and R. D. Lorenz, "Transducer less field orientation
concepts employing saturation-induced saliencies in induction
machines," IEEE Transactions on Industry Applications, vol. 32, pp.
1380-1393, Nov/Dec 1996.
[5] M. Schroedl, "Sensorless control of ac machines at low speed and
standstill based on the "inform" method," in Conference Record of
the31st Annual Meeting of the IAS, vol. 1, pp. 270-277, IEEE, Oct.
1996.
[6] S. Maiti, C. Chakraborty, Y. Hori, and M. C. Ta, "Model reference
adaptive controller-based rotor resistance and speed estimation
techniques for vector controlled induction motor drive utilizing reactive
power," IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 594-601, Feb.
2008.
[7] F. Z. Peng and T. Fukao, "Robust speed identification for speed
sensorless vector control of induction machines," IEEE Transactions on
Industry Applications, vol. IA-30, no. 5, pp. 1234-1230, 1994.
[8] K.Narendra and K.Part-ms arathy, "Identification and control of
dynamical system using neural network" IEEE Trans. Neural Networks,
vol.1, no.1, pp.4-27, Mar.1990
[9] Karanayil M. Rahman M.F. and Grantham C. "Online Stator and Rotor
Resistance Estimation Scheme Using Artificial Neural Networks for
Vector Controlled Speed Sensorless Induction Motor Drive" IEEE
Transactions on Industrial Electronics, vol. 54, no. 1, Feb 2007.
[10] S.K.Mondal, J.O.P.Pinto and B.K. Bose, "A neural network based space
vector PWM controller for a three voltage fed inviter induction motor
drive," IEEE Trans.Ind.Appl. vol. 30.no.3.pp 660-669 May 2002.
[11] Maurizio Cirrincione,and Marcello Pucci, "An MRAS-Based Sensorless
High-Performance Induction Motor Drive With a Predictive Adaptive
Model" IEEE Transactions On Industrial Electronics, vol. 52, pp. 532-
551, Apr 2005.
[12] P.Vas, "Sensorless and Direct Torque Control", Oxford University
Press, 1998.
[13] Nicholas K.Treadgold and Tramas D. Gedeon, "Exploring Constructive
Cascade Networks", IEEE Transactions on Neural Networks, vol. 10,
pp.1335-1349, 1999.
[14] K.S.Narendra and K.Parthasarathy, "Gradient Methods for the
Optimization of Dynamical Systems Containing Neural Networks",
IEEE Transactions on Neural Networks, Vol 2, No.2, pp.252-262,
March 1991.
[15] Martin T. Hagan and Mohammad B. Menhaj, "Training Feedforward
Networks with the Marquardt Algorithm", IEEE Transactions on Neural
Networks, Vol 5,No.6, pp.989-993, November 1994.
[16] Martin T.Hagan, Howard B. Demuth and Mark Beale, "Neural Network
Design", Cengage Learning, Pvt.Ltd., India 2008.
[17] Steven W. Smith, "Digital Signal Processing: a Practical Guide for
Engineers and Scientists", Cengage Learning, Pvt.Ltd., India 2008.
@article{"International Journal of Information, Control and Computer Sciences:61885", author = "K.Sedhuraman and S.Himavathi and A.Muthuramalingam", title = "Performances Comparison of Neural Architectures for On-Line Speed Estimation in Sensorless IM Drives", abstract = "The performance of sensor-less controlled induction
motor drive depends on the accuracy of the estimated speed.
Conventional estimation techniques being mathematically complex
require more execution time resulting in poor dynamic response. The
nonlinear mapping capability and powerful learning algorithms of
neural network provides a promising alternative for on-line speed
estimation. The on-line speed estimator requires the NN model to be
accurate, simpler in design, structurally compact and computationally
less complex to ensure faster execution and effective control in real
time implementation. This in turn to a large extent depends on the
type of Neural Architecture. This paper investigates three types of
neural architectures for on-line speed estimation and their
performance is compared in terms of accuracy, structural
compactness, computational complexity and execution time. The
suitable neural architecture for on-line speed estimation is identified
and the promising results obtained are presented.", keywords = "Sensorless IM drives, rotor speed estimators,
artificial neural network, feed- forward architecture, single neuron
cascaded architecture.", volume = "5", number = "12", pages = "1695-8", }
