Direct torque control for asynchronous machine using Artificial Neural Networks

Author(s):  
Souha Boukadida ◽  
Soufien Gdaim ◽  
Abdellatif Mtibaa
2021 ◽  
Vol 54 (6) ◽  
pp. 881-889
Author(s):  
Kouadria Mohamed Elbachir ◽  
Azaiz Ahmed

Nowadays the multi-inverter multi-machine conventional system takes a great interest of industrials like railway traction. The implementation of single inverter to dual motor makes all the system cheaper; soft operation, more robust and reliable; This paper is one of control methods proposed in the literature to improve performance of this system, a master slave and average control based in artificial neural networks direct torque control of bi asynchronous motors supplied by single three level inverter NPC is discussed. The result of theoretical analysis is tested with MATLAB SIMULINK environment. And through that, the possibility of DTC single inverter multi-motor system has been verified.


In this context, we are taking a close interest in the optimization of wind energy production. It consists in designing simple to implement control strategies of a wind energy conversion system, connected to the network based on the Double Fed Induction Generator (DFIG) driven by the Converter Machine Side (CSM) in order to improve the performance of Direct Torque Control (DTC) and Direct Power Control (DPC). For this purpose, the artificial neural networks (ANNs) is used. Hysteresis comparators and voltage vector switching tables have been replaced by a comparator based on artificial neural networks. The same structure is adopted to build the two neural controllers, for the DTC - ANN and for the DPC - ANN. The simulation results show that the combination of classical and artificial neural network methods permit a double advantage: remarkable performances compared to the DTC-C and DPC-C and a significant reduction of the fluctuations of the output quantities of the DFIG and especially the improvement of the harmonics rate currents generated by the machine.


Author(s):  
Zineb Mekrini ◽  
Seddik Bri

<p><span>This article investigates solution for the biggest problem of the Direct Torque Control on the asynchronous machine to have the high dynamic performance with very simple hysteresis control scheme. The Conventional Direct Torque Control (CDTC) suffers from some drawbacks such as high current, flux and torque ripple, as well as flux control at very low speed. In this paper, we propose an intelligent approach to improve the direct torque control of induction machine which is an artificial neural networks control. The principle, the numerical procedure and the performances of this method are presented.  Simulations results show that the proposed ANN-DTC strategy effectively reduces the torque and flux ripples at low switching frequency, compared with Fuzzy Logic DTC and The Conventional DTC.</span></p>


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