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Título
Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
Autor
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
Version del Editor
Propietario de los Derechos
© 2021 The authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
Aparece en las colecciones
Dateien zu dieser Ressource
Tamaño:
1.150Mb
Formato:
Adobe PDF
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