Archive
Special Issues
Speed Estimation of Three Phase Induction Motor Using Artificial Neural Network
International Journal of Energy and Power Engineering
Volume 3, Issue 2, April 2014, Pages: 52-56
Received: Jan. 26, 2014; Published: Mar. 20, 2014
Views 3283      Downloads 187
Authors
Moinak Pyne, Department of Electrical Engineering, M.C.E.T., West Bengal University of Technology, Kolkata, India
Abhishek Chatterjee, Department of Electrical Engineering, M.C.E.T., West Bengal University of Technology, Kolkata, India
Sibamay Dasgupta, BIEMS, West Bengal University of Technology, Kolkata, India
Article Tools
PDF
Follow on us
Abstract
Three phase induction motors being the most widely used motor for domestic, commercial and industrial applications, demands a more detailed understanding and improved analysis of its performance characteristics. The conventional method of using the equivalent circuit for assessing the motor performance cannot incorporate the non-linearities involved in the speed torque characteristics into the performance of the motor to the fullest extent. This paper presents an ANN based modeling of three phase induction motor to overcome this problem. The model has been tested and validated with actual experimental data. The performance of the model has been compared with that of a classical equivalent circuit technique both graphically and statistically and found to be superior. The model can thus offer a better method of speed estimation and control of the induction motor for input voltage variation with and without input frequency change.
Keywords
Artificial Neural Networks, Three Phase Induction Motor
To cite this article
Moinak Pyne, Abhishek Chatterjee, Sibamay Dasgupta, Speed Estimation of Three Phase Induction Motor Using Artificial Neural Network, International Journal of Energy and Power Engineering. Vol. 3, No. 2, 2014, pp. 52-56. doi: 10.11648/j.ijepe.20140302.13
References
[1]
Vasic, Veran, Slobodan N. Vukosavic and Emil Levi., "A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives", IEEE transactions on Energy Conversion, on 18, no. 4 pp. 476-483, (2003).
[2]
Kohlsnez G. & Fodor D., "Comparison of scalar and vector control strategies of Induction Motor", Hungarian J. Industrial Chemistry, Veszprem, vol. 9 (2) pp. 265-270 (2011).
[3]
Baradwaj, Raj Mohan, Alexander G. Parlos and Hamid A. Toliyat, "Adaptive neural network-based state filter for induction motor speed estimation." In Industrial Electronics Society, 1999. IECON’99 Proceedings. The 25th Annual Conference of the IEEE, vol. 3, pp. 1283-1288. IEEE, 1999.
[4]
Geetha, E. K., T. Thyagarajan, and Vedam Subramanyam, "Robust speed sensorless induction motor drives." In Power Electronics, 2007, ICPE’07, 7th International Conference on Power Electronics, pp. 806-810. IEEE, 2007.
[5]
O. Yuksel, D. Mehmet, "Speed estimation of vector controlled squirrel cage asynchronous motor with artificial neural network." Elsevier Energy Conversion and Management, vol. 52, pp. 675-686, 2009.
[6]
dos Santos, T. H., A. Goedtel, S.A. O. da Silva, and M. Suetake, "A neural speed estimator in Three-Phase Induction Motors powered by a driver with scalar control." In Power Electronics Conference (COBEP), 2011 Brazilian, pp. 44-49. IEEE, 2011.
[7]
S.J. Chapman, Electric Machines Fundamentals, McGrow-Hill (1998) pp. 430-436.
[8]
Neural Network Toolbox TM User Guide, www.mathworks.in/help/pdf_doc/nnet/nnet_ug.pdf
[9]
B.S. Grewal, Higher Engineering Mathematics, Khanna Publishers, 40th Edition.
[10]
Digital Signal Processing Solution for AC Induction Motor, Application Note BPRA043 Texas Instruments.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186