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Showing 3 results for Belaidi
Neuro-Second Order Sliding Mode Control of a DFIG Supplied by a Two-Level NSVM Inverter for Wind Turbine System
H. Benbouhenni, Z. Boudjema, A. Belaidi,
Volume 14, Issue 4 (December 2018)
Abstract
This paper applied second order sliding mode control (SOSMC) strategy using artificial neural network (ANN) on the rotor side converter of a 1.5 MW doubly fed induction generator (DFIG) integrated in a wind turbine system. In this work, the converter is controlled by a neural space vector modulation (NSVM) technique in order to reduce powers ripples and total harmonic distortion (THD) of stator current. The validity of the proposed control technique applied on the DFIG is verified by Matlab/Simulink. The active power, reactive power, torque and stator current are determined and compared with conventional control method. Simulation results presented in this paper shown that the proposed control scheme reduces the THD value and powers ripples compared to traditional control under various operating conditions.

Direct Vector Control of a DFIG Supplied by an Intelligent SVM Inverter for Wind Turbine System
H. Benbouhenni, Z. Boudjema, A. Belaidi,
Volume 15, Issue 1 (March 2019)
Abstract
This article presents an improved direct vector command (DVC) based on intelligent space vector modulation (SVM) for a doubly fed induction generator (DFIG) integrated in a wind turbine system (WTS). The major disadvantages that is usually associated with DVC scheme is the power ripples and harmonic current. To overcome this disadvantages an advanced SVM technique based on fuzzy regulator (FSVM) is proposed. The proposed regulator is shown to be able to reduce the active and reactive powers ripples and to improve the performances of the DVC method. Simulation results are shown by using Matlab/Simulink.

Direct Power Control With NSTSM Algorithm for DFIG Using SVPWM Technique
H. Benbouhenni, Z. Boudjema, A. Belaidi,
Volume 17, Issue 1 (March 2021)
Abstract
The paper presents a super-twisting sliding mode (STSM) regulator with neural networks (NN) of direct power command (DPC) for controlling the active/reactive power of a doubly-fed induction generator (DFIG) using a two-level space vector pulse width modulation (2L-SVPWM). Traditional DPC strategy with proportional-integral (PI) controllers (DPC-PI) has significantly more active/reactive power ripples, electromagnetic torque ripple, and harmonic distortion (THD) of voltages. The proposed DPC strategy based on a neural super-twisting sliding mode controller (NSTSM) minimizes the THD of stator/rotor voltage, reactive/active power ripple, rotor/stator current, and torque ripples. Also, the DPC method with NSTSM controllers (DPC-NSTSM) is a simple algorithm compared to the vector control method. Both methods are developed and programmed in Matlab on a 1.5MW DFIG-based wind turbines. The simulation studies of the DPC technique with the NSTM algorithm have been performed, and the results of these studies are presented and discussed.

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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.