RT info:eu-repo/semantics/doctoralThesis
T1 Diagnóstico de fallos en generadores tipo jaula de ardilla de turbinas eólicas mediante la señal de corriente
A1 Merizalde Zamora, Yury Humberto
A2 Universidad de Valladolid. Escuela de Doctorado
K1 Generadores eléctricos
K1 Mantenimiento (Ingeniería)
K1 Electric generador
K1 Generador eléctrico
K1 Current signal
K1 Señal de corriente
K1 Fault diagnosis
K1 Diagnóstico de fallo
K1 3310.04 Ingeniería de Mantenimiento
AB In relation to maintenance, the main strategy of the wind industry is predictive maintenance based on the constant monitoring of various types of signals obtained from the components of the wind turbines (WTs) through sensors. Since all dynamic equipment produces acoustic or ultrasound vibration, this type of signal is generally used to monitor from the blades to the tower, and most of the existing references on fault detection and diagnosis use the vibration signal. However, there is a lack of publications on other types of signals, especially when it comes to field work. Therefore, this thesis is dedicated exclusively to the study of the current signal and its application to the maintenance of the squirrel-cage induction generator used in WTs. The research includes from the historical aspects of the use of the current signal, theoretical foundations on how the components associated with faults are manifested in the signal spectrum and the methodologies for detection and diagnosis, ranging from techniques for signal processing and traditional artificial intelligence (AI) models, to deep learning models, which represent the state of the art in AI models
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/60301
UL https://uvadoc.uva.es/handle/10324/60301
LA spa
NO Escuela de Doctorado
DS UVaDOC
RD 17-jul-2024