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Doctoral Thesis
DOI
10.11606/T.18.2007.tde-18122007-204855
Document
Author
Full name
Alessandro Goedtel
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2007
Supervisor
Committee
Silva, Ivan Nunes da (President)
Delaiba, Antonio Carlos
Oliveira Júnior, Azauri Albano de
Pinheiro, Carlos Alberto Murari
Serni, Paulo José Amaral
Title in Portuguese
Estimador neural de velocidade para motores de indução trifásicos
Keywords in Portuguese
Estimador de velocidade
Motor de indução trifásico
Redes neurais artificiais
Abstract in Portuguese
Este trabalho apresenta uma estratégia para a estimativa de velocidade do motor de indução trifásico baseada em redes neurais artificiais utilizando medidas de variáveis primárias como tensão e corrente. O uso de motores de indução trifásicos é uma constante em diversos setores industriais e de grande importância no cenário energético nacional. A maioria das metodologias de controle, acionamento e dimensionamento destes motores é fundamentada nas medidas de velocidade no eixo. Entretanto, a medida direta da velocidade compromete o sistema de controle e acionamento diminuindo sua robustez e aumentando o custo de implementação. Resultados de simulação e de ensaios experimentais para validação da proposta são também apresentados.
Title in English
Speed neural estimator for the three-phase induction motors
Keywords in English
Artificial neural networks
Speed estimator
Three-phase induction motors
Abstract in English
This work presents an approach to estimate speed in induction motors based on artificial neural networks and using measurement of primary variables like voltage and current. The use of induction motors is very common in many industrial sectors and plays an important role in the national energetic scene. The methodologies used in control, start up and dimensioning of these motors are based on measure of the speed variable. However, the direct measure of this variable compromises the system control and start up of the machine, reducing its robustness and increasing the implementation costs. Simulation results and experimental data are presented to validate the proposed approach.
 
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Publishing Date
2007-12-20
 
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