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A health condition model for wind turbine monitoring through neural networks and proportional hazard models

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IIT-17-039A.pdf (2.035Mb)
Fecha
2017-10-01
Autor
Mazidi, Peyman
Du, Mian
Bertling Tjemberg, Lina
Sanz Bobi, Miguel Ángel
Estado
info:eu-repo/semantics/publishedVersion
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Resumen
 
 
In this paper, a parametric model for health condition monitoring of wind turbines (HCWT) is developed. The study is based on the assumption that a wind turbine’s (WT) health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated timeseries of differences between neural network predictions and actual measurements. These cumulative signals carry health condition related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the WT. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The HCWT model has capability of evaluating real-time and overall health condition of a WT which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.
 
URI
https:doi.org10.11771748006X17707902
A health condition model for wind turbine monitoring through neural networks and proportional hazard models
Tipo de Actividad
Artículos en revistas
ISSN
1748-006X
Materias/ categorías / ODS
Instituto de Investigación Tecnológica (IIT)
Palabras Clave

Wind Turbine, Condition Monitoring, Prognostics, Maintenance Management, Neural Networks
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Repositorio de la Universidad Pontificia Comillas copyright © 2015  Desarrollado con DSpace Software
Contacto | Sugerencias