Volume 8, Issue 5, October 2019, Page: 85-96
Seven-level NPC Inverter-based Neuronal Direct Torque Control of the PMSM Drives with Regulation Speed Using Neural PI Controller
Habib Benbouhenni, Electrical Engineering Department, National Polytechnique School of Oran Maurice Audin, Oran, Algeria
Received: Sep. 23, 2019;       Accepted: Oct. 18, 2019;       Published: Nov. 26, 2019
DOI: 10.11648/j.ijiis.20190805.11      View  16      Downloads  11
Abstract
In the scope of this work, a new structure of the direct torque control (DTC) technique scheme of permanent magnet synchronous motors (PMSMs) is proposed using a seven-level neutral point clamped (NPC) inverter and artificial neural networks (ANNs) controllers. This intelligent strategy was used to replace, on the one hand the integral proportional (PI) speed controller, on the other hand and the traditional switching table in order to reduce total harmonic distortion (THD) of voltage/current, stator flux ripple and electromagnetic torque ripples. The modeling and simulation of the proposed control scheme were carried out in Matlab/Simulink environment. The simulation results show that the proposed strategy scheme reduced harmonic distortion of stator voltage/current, stator flux and electromagnetic torque ripples compared to traditional DTC control scheme, DTC control scheme with space vector pulse width modulation (SVPWM) and three-level DTC control scheme. It was found that the stator current waveform becomes purely sinusoidal with a reduction in the harmonic distortion rate to 11.77%.
Keywords
PMSMs, DTC, NPC, NNs, SVPWM, PI, THD
To cite this article
Habib Benbouhenni, Seven-level NPC Inverter-based Neuronal Direct Torque Control of the PMSM Drives with Regulation Speed Using Neural PI Controller, International Journal of Intelligent Information Systems. Vol. 8, No. 5, 2019, pp. 85-96. doi: 10.11648/j.ijiis.20190805.11
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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