Rotor Flux Estimation of Induction Motor Using Artificial Neural Networks
DOI:
https://doi.org/10.53555/nneee.v2i11.174Keywords:
Induction Motor, Field Oriented Control, Artificial Neural Network,, Error Back PropagationAbstract
Rotor flux measurement is needed for the control of induction motor by methods like field oriented control. But it is difficult to measure rotor flux in induction motors. Hence rotor flux is estimated by using neural networks in this paper. Rotor flux is simulated using model equations of induction motor and is compared with output of neural network.
References
T.A. Lipo, “Electric Machine Analysis and Simulation,” Research Report, Wisconsin Electric Machines & Power Electronics Consortium,
, pp. 313-324.
K.S. Graeid, H.W. Ping, and H.A.F. Mohamed, “Simulink Representation of Induction Motor Reference Frames,” in Proceedings of the
International Conference for Technical
O. Dordevic, N. Bodo, and M. Jones, “Model of an Induction Machine with an Arbitrary Phase Number in MATLAB / Simulink for
Educational Use,” in Proceedings of the 2010 International Universities Power Engineering Conference (UPEC), 2010, pp. 1-6.
Adkins, B. 1957. "The General Theory of Electrical Machines", Chapman & Hall Ltd
D. A. Kocabas, E. Salman, and A.K. Atalay, "Analysis Using D-Q Transformation of a Drive System Including Load and Two Identical
Induction Motors," in Proceedings of the IEEE International Electric Machines & Drives Conference (IEMDC), 2011, pp. 1575 – 1578.
M. H. Moradi and P.G. Khorasani, “A New MATLAB Simulation of Induction Motor,” in Proceedings of the 2008 Australasian
Universities Power Engineering Conference (AUPEC), 2008, pp. 1-6.
Okoro, O.I., "Dynamic and thermal modelling of induction machine with non-linear effects", Dissertation, University of Kassel, Germany, September 2002.
G. C. Verghese, and R. Sanders, “Observers for flux estimation in induction machines”, IEEE Transactions on Industrial Electronics, Vol. 35, 1988.
K. K. Busawon and M. Saif, “A state observer for nonlinear systems”, IEEE Transactions on Automatic Control, Vol. 44, 1999.
A. Germani, C. Manes, and P. Pepe, “A new approach to state observation of nonlinear systems with delayed output”, IEEE Transactions on Automatic Control, Vol. 47, 2002.
M. Godoy and K. Bose, “Neural network based estimation of feedback signals for a vector controlled induction motor drive”, IEEE
Transaction on Industry applications, Vol 31, 1995, 620-629.
.H. Nguyen and B. Windrow, “Neural networks for self learning control systems,” IEEE Control Syst. Mag., pp. 18-23, Apr. 1990
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