A Novel Switching Tables of Twelve Sectors DTC for Induction Machine Drive Using Artificial Neural Networks
Automation, Control and Intelligent Systems
Volume 7, Issue 1, February 2019, Pages: 1-8
Received: Feb. 23, 2019; Accepted: Apr. 1, 2019; Published: Apr. 26, 2019
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Author
Habib Benbouhenni, Electrical Engineering Department, National Polytechnique School of Oran Maurice Audin, LAAS Research Laboratory, Oran, Algeria
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Abstract
The direct torque control (DTC) is one of the actively researched control schemes of induction machines (IMs), which is based on the decoupled control of stator flux and electromagnetic torque. The traditional twelve sectors DTC control scheme of IM drive using hysteresis comparators and switching table has considerable electromagnetic torque ripple, stator flux ripple and harmonic distortion of voltage/current for IM drive. In order to ensure a robust twelve sectors DTC control scheme and minimize the harmonic distortion of stator current, a novel switching tables of twelve sectors DTC control scheme with the application of the artificial intelligence technique (artificial neural networks (ANNs)). The electromagnetic torque, stator flux and harmonic distortion of stator current are determined and compared with the traditional twelve sectors DTC control scheme. The simulation of the proposed switching tables were carried out in Matlab/Simulink software. A comparative study of the proposed switching tables is also presented to illustrate the merits of each of the switching table on the performance of the twelve sectors DTC control scheme.
Keywords
Direct Torque Control, Induction Motor, Neural Network, Twelve Sectors
To cite this article
Habib Benbouhenni, A Novel Switching Tables of Twelve Sectors DTC for Induction Machine Drive Using Artificial Neural Networks, Automation, Control and Intelligent Systems. Vol. 7, No. 1, 2019, pp. 1-8. doi: 10.11648/j.acis.20190701.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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