Medidas de informação e sistemas inteligentes aplicados no diagnóstico de curto-circuito do estator de motores de indução trifásicos

• Ano de defesa: 2016
• Autor(a) principal: Bazan, Gustavo Henrique
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Cornelio Procopio; Brasil; Programa de Pós-Graduação em Engenharia Elétrica; UTFPR
• Palavras-chave em Português: Motores elétricos de indução; Curtos-circuitos; Redes neurais (Computação); Electric motors, Induction; Short circuits; Neural networks (Computer science); CNPQ::ENGENHARIAS; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/3193

This work proposes the study and development of an alternative approach to diagnose stator short-circuit faults in induction motors driven directly from a supply line. In order to reduce the size and complexity in these types of systems, signal processing techniques of extraction and feature selection are used. In the extraction step, the mutual information of the delayed phases of current signals, ia and ib, are computed and in the selection procedure, the algorithms C4.5 decision tree and multilayer perceptron neural network are employed in order to obtain an effective diagnostic of stator short-circuit faults. To assess the classification accuracy across the various levels of stator short-circuit fault severity (from 1% to 20%), offline and online experimental tests also considered a wide range of load conditions and voltage unbalance in the power supply. The obtained results indicate that this approach can be employed to classify stator short-circuit faults.