Volume 15, Issue 1 (March 2019)                   IJEEE 2019, 15(1): 142-150 | Back to browse issues page

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Lazreg M H, Bentaallah A. Sensorless Speed Control of Double Star Induction Machine With Five Level DTC Exploiting Neural Network and Extended Kalman Filter. IJEEE 2019; 15 (1) :142-150
URL: http://ijeee.iust.ac.ir/article-1-1296-en.html
Sensorless Speed Control of Double Star Induction Machine With Five Level DTC Exploiting Neural Network and Extended Kalman Filter
M. H. Lazreg , A. Bentaallah
Abstract:   (4665 Views)
This article presents a sensorless five level DTC control based on neural networks using Extended Kalman Filter (EKF) applied to Double Star Induction Machine (DSIM). The application of the DTC control brings a very interesting solution to the problems of robustness and dynamics. However, this control has some drawbacks such as the uncontrolled of the switching frequency and the strong ripple torque. To improve the performance of the system to be controlled, robust techniques have been applied, namely artificial neural networks. In order to reduce the number of sensors used, and thus the cost of installation, Extended Kalman filter is used to estimate the rotor speed. By viewing the simulation results using the MATLAB language for the control. The results of simulations obtained showed a very satisfactory behaviour of the machine.
Keywords: Double Star Induction Machine , Direct Torque Control (DTC) , Five Level Inverter , Artificial Neural Network (ANN) , Sensorless Control , Extended Kalman Filter
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Type of Study: Research Paper | Subject: Nonlinear Control Systems
Received: 2018/06/13 | Revised: 2019/02/07 | Accepted: 2018/09/08
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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.