RT info:eu-repo/semantics/article
T1 Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks
A1 Merizalde Zamora, Yury Humberto
A1 Hernández Callejo, Luis
A1 Duque Pérez, Óscar
A1 Alonso Gómez, Víctor
K1 Wind turbines
K1 Artificial intelligence
K1 Inteligencia artificial
K1 Motores de inducción
K1 Faults diagnostic
K1 Synthetic data
K1 3313.30 Turbinas
AB 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.
PB MDPI
SN 2076-3417
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/59537
UL https://uvadoc.uva.es/handle/10324/59537
LA eng
NO Applied Sciences, 2021, Vol. 11, Nº. 15,  6942
NO Producción Científica
DS UVaDOC
RD 06-ago-2024