Showing 8 results for Benbouhenni
H. Benbouhenni,
Volume 14, Issue 1 (March 2018)
Abstract
In this paper, the author proposes a sensorless direct torque control (DTC) of an induction motor (IM) fed by seven-level NPC inverter using artificial neural networks (ANN) and fuzzy logic controller. Fuzzy PI controller is used for controlling the rotor speed and ANN applied in switching select stator voltage. The control method proposed in this paper can reduce the torque, stator flux and total harmonic distortion (THD) value of stator current, and especially improve system good dynamic performance and robustness in high and low speeds.
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.
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.
H. Benbouhenni,
Volume 15, Issue 3 (September 2019)
Abstract
This article presents a sliding mode control (SMC) with artificial neural network (ANN) regulator for the doubly fed induction generator (DFIG) using two-level neural pulse width modulation (NPWM) technique. The proposed control scheme of the DFIG-based wind turbine system (WTS) combines the advantages of SMC control and ANN regulator. The reaching conditions, robustness and stability of the system with the proposed control are guaranteed. The SMC method which is insensitive to uncertainties, including parameter variations and external disturbances in the whole control process. Finally, the SMC control with neural network regulator (NSMC) is used to control the stator reactive and a stator active power of a DFIG supplied by the NPWM strategy and confirms the validity of the proposed approach. Results of simulations containing tests of robustness and tracking tests are presented.
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.
Z. Boudjema, H. Benbouhenni, A. Bouhani, F Chabni,
Volume 17, Issue 3 (September 2021)
Abstract
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.
H. Benbouhenni, N. Bizon, I. Colak,
Volume 18, Issue 3 (September 2022)
Abstract
The space vector modulation (SVM) method was recently proposed and captured the interest of scientific research in the following years. In this paper, besides a brief review of the SVM methods proposed in the literature, a new SVM strategy based on the calculation of the minimum (Min) and maximum (Max) of three-phase voltages is proposed. The proposed SVM technique does not have to calculate the sector and angle, as is done in the traditional SVM technique. Therefore, it is the easiest technique to accomplish compared to the traditional SVM method and other existing methods. Compared with the traditional pulse width modulation (PWM), the advantage of using this new SVM strategy is that the scheme is simple and the total harmonic distortion (THD) value in the output of the two-level inverter is minimized. The technology has been simulated by MATLAB/Simulink, and then implemented on a real traditional two-level inverter using the dSPACE card. It is worth reporting the reduction obtained for THD using the proposed SVM technique (where THD is about 70%) compared to the traditional PWM technique (where THD is about 79.5%).
G. Hamza, M. Sofiane, H. Benbouhenni, N. Bizon,
Volume 19, Issue 2 (June 2023)
Abstract
In this paper, a wind power system based on a doubly-fed induction generator (DFIG) is modeled and simulated. To guarantee high-performance control of the powers injected into the grid by the wind turbine, five intelligent super-twisting sliding mode controllers (STSMC) are used to eliminate the active power and current ripples of the DFIG. The STSMC controller is a high-order sliding mode controller which offers high robustness compared to the traditional sliding mode controller. In addition, it reduces the phenomenon of chattering due to the discontinuous component of the SMC technique. However, the simplicity, ease of execution, durability, and ease of adjusting response are among the most important features of this control compared to some other types. To increase the robustness and improve the response of STSMC, particle swarm optimization method is used for this purpose, where this algorithm is used for parameter calculation. The simulation results obtained using MATLAB software confirm the characteristics of the designed strategy in reducing chattering and ensuring good power control of the DFIG-based wind power.
