Novel SNC-NN-MRAS Based Speed Estimator for Sensor-Less Vector Controlled IM Drives

Rotor Flux based Model Reference Adaptive System (RF-MRAS) is the most popularly used conventional speed estimation scheme for sensor-less IM drives. In this scheme, the voltage model equations are used for the reference model. This encounters major drawbacks at low frequencies/speed which leads to the poor performance of RF-MRAS. Replacing the reference model using Neural Network (NN) based flux estimator provides an alternate solution and addresses such drawbacks. This paper identifies an NN based flux estimator using Single Neuron Cascaded (SNC) Architecture. The proposed SNC-NN model replaces the conventional voltage model in RF-MRAS to form a novel MRAS scheme named as SNC-NN-MRAS. Through simulation the proposed SNC-NN-MRAS is shown to be promising in terms of all major issues and robustness to parameter variation. The suitability of the proposed SNC-NN-MRAS based speed estimator and its advantages over RF-MRAS for sensor-less induction motor drives is comprehensively presented through extensive simulations.

Authors:

• A.Venkadesan
• S.Himavathi
• A.Muthuramalingam

Keywords:

• Sensor-less operation
• vector-controlled IM drives
• SNC-NN-MRAS
• single neuron cascaded architecture
• RF-MRAS
• artificial neural network

References:

[1] Shady M.Gadoue, Damian Giaouris, and John W.Finch, "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.
[2] V. Vasic and S. Vukosavic uder, "Robust MRAS-Based algorithm for
Stator Resistance and Rotor Speed Identification", IEEE Power Eng.
Rev., pp. 39-41, Nov. 2001.
[3] Colin Schauder, "Adaptive Speed Identification for Vector Control of
Induction Motors without Rotational Transducers", IEEE Transactions
on Industry Applications, Vol. 28, No.5, pp.1054-1061,
September/October 1992.
[4] 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 Transactions on Industrial Electronics, Vol.55,
No.2, pp. 594-601,Feb. 2008.
[5] P. Vas, "Sensorless Vector and Direct Torque Control",
NewYork:Oxford Univ. Press, 1998.
[6] J.Holtz and J.Quan, "Drift- and Parameter-Compensated Flux Estimator
for Persistent Zero-Stator- Frequency Operation of Sensorless-
Controlled Induction Motors", IEEE Transactions on Industrial
Applications, Vol.39, No.4, pp. 1052-1060,Jul./Aug. 2003.
[7] Bimal K. Bose, "Modern Power Electronics and AC Drives", Prentice-
Hall, Pvt.Ltd., India 2005.
[8] L.Ben-Brahim,S.Tadakuma, and A.Akdag, "Speed Control of Induction
Motor Without Rotational Transducers", IEEE Transactions on Industry
Applications,Vol.35, No.4, pp.844-850, Jul./Aug.1999.
[9] B.K.Bose and N.R.Patel, "A Programmable Cascaded Low-Pass Filter-
Based Flux Synthesis for a Stator Flux-Oriented Vector-Controlled
Induction Motor Drive", IEEE Transactions on Industrial Electronics,
Vol.44, no.1, pp.140-143, 1997.
[10] J.Hu and B.Wu, "New Integration Algorithms for Estimating Motor Flux
over a Wide Speed Range", IEEE Transactions on Power Electronics,
Vol.13, No.5, pp.969-977, September 1998.
[11] K.S.Narendra, and K.Parthasarathy, "Identification and Control of
Dynamical Systems Using Neural Networks", IEEE Transactions on
Neural Networks, Vol.1, No.1, pp.4-27, Mar. 1990.
[12] Luiz.E.B.daSilva, B.K.Bose and Joao.o.p.Pinto, "Recurrent-Neural-
Network-Based Implementation of a Programmable Cascaded Low-Pass
Filter Used in Stator Flux Synthesis of Vector Controlled Induction
Motor Drive", IEEE Transactions on Industrial Electronics, Vol.46,
No.3, pp.662-665, 1999.
[13] A.Muthuramalingam, A.Venkadesan, and S.Himavathi, "On-Line Flux
Estimator using Single Neuron Cascaded Neural Network Model for
Sensor-less Vector Controlled Induction Motor Drives", Proc.
International Conference on System Dynamics and Control (ICSDC-
2010), Manipal Insititute of Technology, Manipal, India, pp.96-100,
2010.
[14] Nicholas K.Treadgold, and Tamás D. Gedeon, "Exploring Constructive
Cascade Networks", IEEE Transactions on Neural Networks, Vol.10,
No.6, pp.1335-1350, Nov. 1999.