FIVE-LEVEL DTC WITH 12 SECTORS OF INDUCTION MOTOR DRIVE USING NEURAL NETWORKS CONTROLLER FOR LOW TORQUE RIPPLE

This paper presents an improved five-level DTC with 12 sectors of induction motor based on artificial neural networks controller. This neural controller was used to replace on the conventional comparator hysteresis of torque in order to reduce torque ripple, flux and stator current. The validity of the proposed control scheme is verified by simulation tests of an induction motor drive system. The stator current and stator flux, torque is determined and compared to the above techniques using Matlab/Simulink environment

FPWM TECHNIQUE BASED CONVERTER FOR IM DRIVES

This article presents an improved pulse width modulation (PWM) based on fuzzy logic (FL) of induction motor (IM). The major problem that is usually associated with PWM technique is the high total harmonic distortion (THD), stator flux ripple and electromagnetic torque ripples. To overcome these problems a PWM strategy is proposed based on the fuzzy logic controller (FPWM). The fuzzy proposed controller is shown to be able to reduce the THD of stator current, electromagnetic torque ripple and stator flux ripple. The simulation results are shown by using MATLAB/SIMULINK software

Experimental assessment of a dual super-twisting control technique of variable-speed multi-rotor wind turbine systems

Experimental work using Hardware-in-the-loop simulation is performed in this article for a power system based on multi-rotor wind energy for power generation. Power is generated using a doubly-fed induction generator (DFIG), where the suggested approach to regulate the energy is different from the direct power command (DPC) in terms of idea and structure, even though the same equations are used to estimate the powers. Two parallel super-twisting controls are used to control the power, and modified space vector modulation (MSVM) is used to operate the rotor side converter. The designed command is unrelated to the system’s mathematical model and uses only a few gains, which makes it an effective and distinctive effectiveness compared to the DPC based on the super-twisting controller (DPC-STC). Also, simplicity, robustness, and ease of application are the most notable characteristics of the suggested command. Moreover, an uncontrolled grid-side converter was used to demonstrate the efficacy and competence of the suggested command in ameliorating the features of the designed power system. The suggested command was first verified in MATLAB, and the results were confirmed using experimental work, where Hardware-in-the-loop using the dSPACE 1104 was used for this purpose. In this work, the results obtained with the DPC-STC technique were compared in terms of undulations and oscillation minimization ratio, total harmonic distortion (THD) of stream, and steady-state error (SSE) value. The designed command minimized the THD of current with an efficiency of 45.94%, 46.95%, and 45.93% in the first test, second test, and third test, respectively. Also, the active power undulations were minimized compared to the DPC-STC by58.33%, 45.60%, and 44.88% in the first test, second test, and third test, respectively. The SSE of reactive power was also reduced by 68%, 71.32%, and 70.80% in the first test, second test, and third test, respectively. These ratios indicate the effectiveness, ability, and efficiency of the suggested command to ameliorate the efficiency of the power system

Régulateurs Pi-flou Pour La Commande Dtc Neuronale Sans Vecteurs Nuls à 12 Secteurs D’une Mas De Forte Puissance

Ce travail présente une stratégie de commande directe du couple (DTC) par l’intelligence artificielle (Réseaux de neurones (RNA) et la logique floue (FL)) appliquée au système du contrôle d’une machine asynchrone de forte de puissance. L’application de la DTC apporte un résultat très intéressant aux obstacles de robustesse et de dynamique. Une comparaison des performances de la commande DTC proposée avec la commande DTC classique sans vecteur nuls à 12 secteurs sera présentée. Les résultats de simulation ont été visualisés et présentées par Matlab/Simulink. Les ondulations du courant, du flux, et du couple seront jugées et comparées pour les deux commandes proposées. Comme résultats, le comportement de la DTC basés sur les RNA et FL est plus performant par rapport à la commande DTC conventionnelle

IMPROVED SWITCHING SELECTION FOR DTC OF INDUCTION MOTOR DRIVE USING ARTIFICIAL NEURAL NETWORKS

Direct Torque Control (DTC) is a control technique in AC drive systems to obtain high performance torque ripple. This paper also proposes improvement of the conventional DTC without voltages zeros using the improvement of the switching table and the application of the Artificial Neural Network (ANN) to minimize the torque ripple, stator flux ripple and Total Harmonic Distortion (THD) value of stator current and to get better performance of the induction motor (1MW) controlled by DTC, by using two-level inverter. The comparison with conventional direct torque control, show that the use of the proposed strategies with ANN, reduced the torque ripple, stator flux ripple and total harmonic distortion value of stator current. The validity of the proposed strategies is confirmed by the simulative results

Improved Rotor Flux and Torque Control Based on the Third-Order Sliding Mode Scheme Applied to the Asynchronous Generator for the Single-Rotor Wind Turbine

In this work, a third-order sliding mode controller-based direct flux and torque control (DFTC-TOSMC) for an asynchronous generator (AG) based single-rotor wind turbine (SRWT) is proposed. The traditional direct flux and torque control (DFTC) technology or direct torque control (DTC) with integral proportional (PI) regulator (DFTC-PI) has been widely used in asynchronous generators in recent years due to its higher efficiency compared with the traditional DFTC switching strategy. At the same time, one of its main disadvantages is the significant ripples of magnetic flux and torque that are produced by the classical PI regulator. In order to solve these drawbacks, this work was designed to improve the strategy by removing these regulators. The designed strategy was based on replacing the PI regulators with a TOSMC method that will have the same inputs as these regulators. The numerical simulation was carried out in MATLAB software, and the results obtained can evaluate the effectiveness of the designed strategy relative to the traditional strategy

Advanced Direct Vector Control Method for Optimizing the Operation of a Double-Powered Induction Generator-Based Dual-Rotor Wind Turbine System

The main goal of this paper is to increase the active/reactive power extracted from variable-speed dual-rotor wind power (DRWP) based on doubly-fed induction generators (DFIG) by optimizing its operation using advanced direct vector control. First, the dynamic modeling of different parts of the system is introduced. The DFIG is modeled in the Park reference system. After that, the control techniques are introduced in detail. Direct vector command (DVC) with four-level fuzzy pulse width modulation (FPWM) is used to control the rotor current, thereby controlling the reactive power and active power of the generator. Then, use the neural network design to replace the traditional proportional-integral (PI) controller. Finally, the Matlab/Simulink software is used for simulation to prove the effectiveness of the command strategy using 1.5 MW DRWP. The results show good performance in terms of response time, stability, and precision in following the reference under variable wind speed conditions. In addition, the total harmonic distortion (THD) value of stator current is about 0.13%, being a bit less than other THD values reported in the literature