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Document Type : Latin Dissertation
Language of Document : English
Record Number : 151366
Doc. No : ET23158
Main Entry : Edina Bajrektareviغ
Title Proper : Parameter Identification of Induction Motor Using a Genetic Algorithm
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : High performance variable-speed machines incorporate a model for the system ineither the controller or state estimation stages. The accuracy and general robustness of themachine is dependant on this model. Therefore, it must accurately represent both theelectrical and electromagnetic interactions within the machine and associated mechanicalsystems. Recently, some new technologies have been tested in the field of electromechanics like neural networks, fuzzy logic, simulated annealing and genetic algorithms.These methods are increasingly being utilized in solving electric machine problems.In this thesis, a genetic algorithm (GA) ت a form of artificial intelligence ت isemployed to identify the electric parameters of induction motors. The variables used tocalculate the electric parameters are the measured stator currents, stator voltages androtor speed. The variables are acquired by using Data Acquisition System and Lab VIEWSoftware. Free acceleration test is performed on 7.5 hp induction motor, using a constantfrequency power supply. The performance of the identification scheme is demonstratedwith simulated and measured data, and electric parameters obtained using this method arecompared with parameters obtained from IEEE standard tests. Based on the results, themethod proved to be worth considering in optimizing induction machines and can beapplied to a variety of induction motor parameter estimation problems.....-....,....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject : Electericl tess
: برق
electronic file name : TL46394.pdf
Title and statement of responsibility and : Parameter Identification of Induction Motor Using a Genetic Algorithm [Thesis]
 
 
 
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