WSEAS Transactions on Power Systems


Print ISSN: 1790-5060
E-ISSN: 2224-350X

Volume 13, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.


Artificial Neural Network and Adaptive Neuro Fuzzy Control of Direct Torque Control of Induction Motor for Speed and Torque Ripple Control

AUTHORS: V. M. Venkateswara Rao, G. Chandra Sekhar, Y. P. Obulesh

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ABSTRACT: This paper presents Artificial Neural Networks (ANN) and Adaptive Neural-Fuzzy Inference System (ANFIS) for reduction of torque and flux ripples in transient and steady state response of Direct Torque Control (DTC) for Induction Motor drive. The Flux and Electromagnetic torque can be controlled by using efficient Direct Torque Control (DTC) scheme This proposed technique is to improve the torque, speed and flux response with the Artificial Neural Network (ANN) and then with the Adaptive Neuro-Fuzzy Inference (ANFIS). This paper shows implementation of DTC system using ANN and ANFIS on three phase induction motor to optimize the flux and to improve the performance of fast stator flux response in transient state. To improve the performance of DTC with the modern technique using ANN and ANFIS approach is implemented and performance of ANN DTC compared with CDTC and ANN DTC with ANFIS is done, conclusion is about the ANN approach shows the better performance than CDTC and ANFIS shows superior performance than ANN. The performance has been tested by using MATLAB/SIMULINK and NEURAL NETWORK toolbox

KEYWORDS: Direct torque Control, Induction Motor, Fuzzy Logic Controller, ANN, ANFIS

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WSEAS Transactions on Power Systems, ISSN / E-ISSN: 1790-5060 / 2224-350X, Volume 13, 2018, Art. #40, pp. 414-421


Copyright © 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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