KUET Institutional Repository

Quantum Evolutionary Algorithm Based High Performance Control of Induction Motor Drive Using ANN Flux Estimator

Show simple item record

dc.contributor.advisor Rafiq, Prof. Dr. Md. Abdur
dc.contributor.author Hahibullab, Md.
dc.date.accessioned 2018-08-09T12:14:59Z
dc.date.available 2018-08-09T12:14:59Z
dc.date.copyright 2012
dc.date.issued 2012-05
dc.identifier.other ID 0000000
dc.identifier.uri http://hdl.handle.net/20.500.12228/263
dc.description This thesis is submitted to the Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Electronic Engineering, May 2012. en_US
dc.description Cataloged from PDF Version of Thesis.
dc.description Includes bibliographical references (pages 54-57).
dc.description.abstract In this study, Quantum Evolutionary Algorithm (QEA) based high performance control of induction motor is proposed, whose rotor flux is estimated by chaotic learning based Artificial Neural Network (ANN). The control principle is based on Direct Torque Control (DTC) with Space Vector Modulation (SVM) technique. The SVM reduces the torque and flux ripples and improves steady state performance. Fast dynamic speed response is obtained through maintaining the rotor flux constant as in the case of field orientation control. Main flux saturation effect is also considered throughout the study for more realistic representation in the analysis. QEA based proportional-integral (P1) controller tuning is used for getting optimized gain coefficients of P1 controller which also help us to get fast speed response induction motor drive. The performance of the drive system with QEA based P1 controller is compared with Conventional Genetic Algorithm (CGA) based P1 controller. There is no speed fluctuation in the speed response of QEA based induction motor drive under steady state condition whereas a little bit speed fluctuation is present in the CGA based induction motor drive. 4 In this work, chaotic learning based Artificial Neural Networks (ANNs) such as Backpropagation (BP), Real Time Recurrent Learning (RTRL). and Correlated Real Time Recurrent Learning (CRTRL) are proposed for improved rotor flux estimation of induction motor drive which makes the control system position sensorless. Chaotic variations of learning rate are included with the learning rate of BP, RTRL, and CRTRL algorithms based ANNs which generates a chaotic time series and a rescaled version of the series is used as Learning Rate (LR) during the training process. We have shown the improvement of BP and CRTRL algorithm based ANNs in rotor flux estimation of induction motor drive due to the use of chaotic variations in learning rate. A high performance simple speed estimator is also presented in this work. It is confirmed that the proposed speed estimator is capable to estimate the speed accurately even at very low speed. Effectiveness of the proposed controller is tested by simulation for different set speed. This simple speed estimator for the induction motor drive makes the controller cost effective and sensorless. Robustness of the control system is tested by using the parameter perturbation and applying sudden load torque disturbance within very short time interval. The control system is also computationally efficient and tested by introducing 10% instrumental error to reference voltage vectors. en_US
dc.description.statementofresponsibility Md. Hahibullab
dc.format.extent 59 pages
dc.language.iso en_US en_US
dc.publisher Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh. en_US
dc.rights Khulna University of Engineering & Technology (KUET) thesis/ dissertation/internship reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subject Quantum Evolutionary Algorithm en_US
dc.subject High Performance Control en_US
dc.subject Induction Motor Drive en_US
dc.subject Artificial Neural Network (ANN) en_US
dc.title Quantum Evolutionary Algorithm Based High Performance Control of Induction Motor Drive Using ANN Flux Estimator en_US
dc.type Thesis en_US
dc.description.degree Master of Science in Electrical and Electronic Engineering
dc.contributor.department Department of Electrical and Electronic Engineering


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search KUET IR


Browse

My Account

Statistics