In this paper, sensor drift compensation of vector control of induction motor using neural network is presented. An induction motor is controlled based on vector control. The sensors sense the primary feedback signals for the feedback control system which is processed by the controller. Any fault in the sensors cause incorrect measurements of feedback signals due to malfunction in sensor circuit elements which affects the system performance. Hence, sensor fault compensation or drift compensation is important for an electric drive. Analysis of sensor drift compensation in motor drives is done using neural networks. The feedback signals from the phase current sensors are given as the neural network input. The neural network then performs the auto-associative mapping of these signals so that its output is an estimate of the sensed signals. Since the Auto-associative neural network exploits the physical and analytical redundancy, whenever a sensor starts to drift, the drift is compensated at the output, and the performance of the drive system is barely affected.

1. A.K.Sharma,R.A.Gupta, Laxmi Srivastava, “Performance of ANN based indirect vector control induction motor drive”, Journal of theoritical and applied Information technology,2007.
2. L.Galotto, J.O.P. Pinto, B. Ozpineci, L.C.Leite, L.E. Borges da silva, “Sensor compensation in motor drives using Kernel Regression”, IEEE International Electric Machines and drives conference,2007.
3. L. Galotto, Bimal. K. Bose, L.C. Leite, J.O.P. Pinto, L.E. Borges da silva, Germano Lambert-Torres, “Auto-Associative Neural Network based sensor drift compensation in Indirect controlled drive system”, IEEE Industrial Electronics society,2007.
4. A. El-Antably, L. Xiaogang and R. Martin, “System simulation of fault conditions in the components of the electric drive system”, IEEE 1993.
5. N. Reitere, D. Roye and P. Mannevy, “Vector based investigation of induction motor drive under fault operations”, IEEE 1997.
6. Kramer. M .A,” Auto-associative Neural Networks”, AIChE Journal, Vol.16.
7. X.Xu, R.N.De Donker and D.W.Novotny, “A stator flux oriented induction machine drive”, IEEE 1998.
8. J.O.P.Pinto, B.K.Bose, L.E.Borges, “ A stator flux oriented vector controlled induction motor drive with space-vector PWM and flux vector synthesis by neural networks”, IEEE 2001.
9. A. El-Antably, L. Xiaogang, and R. Martin, “System simulation of fault conditions in the components of the electric drive system of an electric vehicle of an industrial drive”, IEEE-IECON Conf Rec., pp. 1146-1150, 1993.
10. J.O.P.Pinto, B.K.Bose, L.E.Borges, and M.P.Kazmierkowski, “ A neural network based space vector PWM controller for voltage fed induction motor drive”, IEEE 2000
11. M. G. Simoes and B. K. Bose, Neural network based estimation of feedback signals for a vector controlled induction motor drive", IEEE Trans. Ind. Appl., vol. 31, pp. 620-629, May/June 1995.
12. B.K.Bose, Ed., Power Electronics and variable frequency drives.
13. B.K.Bose., Modern Power Electronics and AC Drives.

Auto Associative Neural Network (AANN) Induction Motor Vector control Sensor Drift Compensation