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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/59537

    Título
    Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
    Autor
    Merizalde Zamora, Yury Humberto
    Hernández Callejo, LuisAutoridad UVA Orcid
    Duque Pérez, ÓscarAutoridad UVA Orcid
    Alonso Gómez, VíctorAutoridad UVA Orcid
    Año del Documento
    2021
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Applied Sciences, 2021, Vol. 11, Nº. 15, 6942
    Zusammenfassung
    To ensure the profitability of the wind industry, one of the most important objectives is to minimize maintenance costs. For this reason, the components of wind turbines are continuously monitored to detect any type of failure by analyzing the signals measured by the sensors included in the condition monitoring system. Most of the proposals for the detection and diagnosis of faults based on signal processing and artificial intelligence models use a fault-free signal and a signal acquired on a system in which a fault has been provoked; however, when the failures are incipient, the frequency components associated with the failures are very close to the fundamental component and there are incomplete data, the detection and diagnosis of failures is difficult. Therefore, the purpose of this research is to detect and diagnose failures of the electric generator of wind turbines in operation, using the current signal and applying generative adversarial networks to obtain synthetic data that allow for counteracting the problem of an unbalanced dataset. The proposal is useful for the detection of broken bars in squirrel cage induction generators, which, according to the control system, were in a healthy state.
    Materias (normalizadas)
    Wind turbines
    Artificial intelligence
    Inteligencia artificial
    Motores de inducción
    Materias Unesco
    3313.30 Turbinas
    Palabras Clave
    Faults diagnostic
    Synthetic data
    ISSN
    2076-3417
    Revisión por pares
    SI
    DOI
    10.3390/app11156942
    Version del Editor
    https://www.mdpi.com/2076-3417/11/15/6942
    Propietario de los Derechos
    © 2021 The authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/59537
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP42 - Artículos de revista [291]
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    Dateien zu dieser Ressource
    Nombre:
    Diagnosis-of-Broken-Bars-in-Wind-Turbine-Squirrel-Cage-Induction-Generator.pdf
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    1.150Mb
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