Volume 17, Issue 3 (September 2021)                   IJEEE 2021, 17(3): 1793-1793 | Back to browse issues page

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Boudjema Z, Benbouhenni H, Bouhani A, Chabni F. DSPACE Implementation of a Neural SVPWM Technique for a Two Level Voltage Source Inverter. IJEEE 2021; 17 (3) :1793-1793
URL: http://ijeee.iust.ac.ir/article-1-1793-en.html
DSPACE Implementation of a Neural SVPWM Technique for a Two Level Voltage Source Inverter
Z. Boudjema , H. Benbouhenni , A. Bouhani , F Chabni
Abstract:   (3092 Views)
This article presents the implementation of an improved space vector pulse width modulation  (SVPWM) technique based on neural network for a real two level voltage source inverter (VSI) realized in our Lab. The major goal of using this new technique is the amelioration of the voltage quality in the output of the VSI by decreasing the effect of the harmonics. The used technique has been simulated by MATLAB/Simulink and then implemented using a DSPACE card on a real two level VSI. The advantages of the used technique are shown by simulation and experiment results.
Keywords: Space Vector Pulse Width Modulation , Two Level Voltage Source Inverter , Neural Network , DSPACE Card
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  • The original contribution of this paper is the application of the artificial neural networks (ANN) in the space vector pulse width modulation (SVPWM) and experimental investigation of this novel method.
  • A SVPWM method based on ANN controller is developed for two-level inverter and compared with conventional SVPWM strategy. On the other hand, the proposed technique is compared to pulse width modulation (PWM).
  • The proposed SVPWM method minimized the total harmonic distortion (THD) compared to the SVPWM and PWM.
  • The proposed technique is simple and easy to implement.
  • The proposed method is robust compared to traditional SVPWM and PWM techniques.
  • In this paper, we propose a new SVPWM technique of VSI.

Type of Study: Research Paper | Subject: Converters
Received: 2020/01/27 | Revised: 2020/10/22 | Accepted: 2020/10/23
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.