Abstract
In this investigation, a vector controlled induction motor drive is simulated and the feedback signals of this vector controlled drive are estimated using neural networks. The neural networks receive the machine terminal signals as inputs and estimate the rotor flux and unit vectors cosθ e and sinθ e as outputs. These outputs are used in the vector controlled drive system. The calculated feedback signals by the neural networks are not sensitive to the motor parameter variations. In this paper, three types of neural networks (i.e. multilayer perceptron (MLP), radial basis function (RBF) and wavenet) are used and the obtained results are compared. Finally, on the basis of the advantages of wavenets, the results prove the accuracy and effectiveness of the wavenet based estimator.
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References
Wade, S., Dunnigan, M.W.: Modeling and Simulation of Induction Machine Vector Control with Rotor Resistance Identification. IEEE Transaction on Power Electronics 12, 495–506 (1997)
Pinto, J.O.P., Bose, B.K., da Silva, L.E.B.: A Stator-Flux-Oriented Vector-Controlled Induction Motor Drive With Space-Vector PWM and Flux Vector Synthesis by Neural Networks. IEEE Transaction on Industry Application 37, 1308–1318 (2001)
Mondal, S.K., Pinto, J.O.P., Bose, B.K.: A Neural-Network-Based Space Space-Vector PWM Controller for a Three-Level Voltage-Fed Inverter Induction Motor Drive. IEEE Transaction on Industry Application 38, 660–669 (2002)
Karanayil, B., Rahman, M.F., Grantham, C.: Stator and Rotor Resistance Observers for Induction Motor Drive Using Fuzzy Logic and Artificial Neural Networks. IEEE Transaction on Energy Conversion 20, 771–780 (2005)
Simoes, M.G., Bose, B.K.: Neural Network Based Estimation of Feedback Signals for a Vector Controlled Induction Motor Drive. IEEE Transaction of Industry Application 31, 620–629 (1995)
Wishart, M.T., Harley, R.G.: Identification and Control of Induction Machines Using Artificial Neural Networks. IEEE Transaction on Industry Application 31, 612–619 (1995)
Tadakuma, S., Tanaka, S., Naitoh, H., Shimane, K.: Improvement of Robustness of Vector-controlled Induction Motor Using Feed Forward and Feedback Control. IEEE Transaction on Power Electronics 12, 221–227 (1997)
Safavi, A.A., Romagnoli, J.A.: Application of Wavelet-based Neural Network to the Modeling and Optimization of an Experimental Distillation. Engineering Application of Artificial Intelligence 10, 301–313 (1997)
Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)
Haykin, S.: Neural Networks. Macmillan College Publishing Company (1999)
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Moghbelli, H., Rahideh, A., Safavi, A.A. (2009). Using Wavelet Based Neural Networks for Feedback Signals Estimation of a Vector Controlled Induction Motor Drive. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_96
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DOI: https://doi.org/10.1007/978-3-642-01507-6_96
Publisher Name: Springer, Berlin, Heidelberg
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