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Study of Artificial Neural Network and Observer-Based High Performance Induction Motor Drives

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dc.contributor.advisor Ghosh, Prof. Dr. Bashudeb Chandra
dc.contributor.author Rafiq, Md. Abdur
dc.date.accessioned 2018-08-11T06:10:58Z
dc.date.available 2018-08-11T06:10:58Z
dc.date.copyright 2001
dc.date.issued 2001-03
dc.identifier.other ID 943002
dc.identifier.uri http://hdl.handle.net/20.500.12228/308
dc.description This thesis is submitted to the Department of Electrical and Electronic Engineering, Bangladesh Institute of Technology (BIT), Khulna in partial fulfillment of the requirements for the degree of Master of Science in Engineering, March, 2001. en_US
dc.description Cataloged from PDF Version of Thesis.
dc.description Includes bibliographical references (pages 92-95).
dc.description.abstract The main subject matter of this dissertation is to study the performance of artificial neural network and observer based high performance induction motor drive. Four suitable flux observers compatible with drive control law are discussed and flux estimation with these observers along with effectiveness is studied. Study of the artificial neural networks for flux estimation with baekpropagation training algorithm for simulation is presented in this dissertation. It also presents the general idea about feedforward neural networks, mapping and training of an artificial neural network. The direct and indirect field orientation control methods of induction motor For variable operating conditions are evaluated in this study. In the direct method, flux estimation is applied for vector rotators which controls drive current or voltage magnitude as well as position SO that the rotor flux can be kept constant. In the indirect method flux estimation is used for parameter compensation. Digital simulation procedures are presented to study the performance of these observerbased field oriented induction motor drives. Speed of an induction machine is also estimated with full order observer and parameter adaptation is also presented for sensorless field orientation control. 1'he main cirawbuck of indirect method of field orientation is due to Variation of rotor resistance that degrades performance and requires tuning. Observers are used for detecting the parameter mismatch condition and correcting the controller resistance. By flux feedback the rotor resistance is adapted and the effectiveness of observers is also examined. Reduced order observer in generalized form is used for parameter adaptation of current source inverter fed system. ix Flux estimation with artificial neural network has been carried out and extended to direct field orientation of voltage source inverter fed induction motor system. Finally, comparison with the results obtained by artificial neural network is given. en_US
dc.description.statementofresponsibility Md. Abdur Rafiq
dc.format.extent 95 pages
dc.language.iso en_US en_US
dc.publisher Bangladesh Institute of Technology (BIT), 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 Artificial Neural Network en_US
dc.subject Motor en_US
dc.subject Induction Motor Drive en_US
dc.subject Networking en_US
dc.title Study of Artificial Neural Network and Observer-Based High Performance Induction Motor Drives en_US
dc.type Thesis en_US
dc.description.degree Master of Science in Engineering
dc.contributor.department Department of Electrical and Electronic Engineering

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