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Machine learning techniques for wind turbine fault diagnosis

Author
Vidal, Y.; Pozo, F.; Luo, N.; Tutivén, C.; Rodellar, J.
Type of activity
Presentation of work at congresses
Name of edition
7th World Conference on Structural Control and Monitoring
Date of publication
2018
Presentation's date
2018-07
Book of congress proceedings
Control and monitoring: abstracts and papers of the 7th World Conference on Structural Control and Monitoring: 7WCSCM: July 22-25, 2018 Qingdao, China
First page
1385
Last page
1394
Project funding
2017 SGR 388: Control, Modelitzacio´, Identificacio´ i Aplicacions (CoDAlab)
Characterization and automatic classification of leukemic cells by means of digital image processing and pattern recognition for diagnosis support
DPI2017-82930-C2-1-R DESARROLLO Y VALIDACION DE SISTEMAS DE MONITORIZACION INTELIGENTE, ESTRATEGIAS DE CONTROL DEL PITCH Y DE AMORTIGUACION ESTRUCTURAL PARA AEROGENERADORES OFFSHOREFLOTANTES
Development and validation of failure detection and design of fault-tolerant control strategies with application in offshore wind energy plants
Repository
http://hdl.handle.net/2117/121613 Open in new window
Abstract
The reliability requirements of wind turbine (WT) components have increased significantly in recent years in the search for a lower impact on the cost of energy. In addition, the trend towards larger WTs installed in offshore locations has significantly increased the cost of repair of the components. In the wind industry, therefore, condition monitoring is crucial for maximum availability. In this work, without using specific tailored devices for condition monitoring but only increasing the samp...
Citation
Vidal, Y., Pozo, F., Luo, N., Tutivén, C., Rodellar, J. Machine learning techniques for wind turbine fault diagnosis. A: World Conference on Structural Control and Monitoring. "Control and monitoring: abstracts and papers of the 7th World Conference on Structural Control and Monitoring: 7WCSCM: July 22-25, 2018 Qingdao, China". 2018, p. 1385-1394.
Keywords
fault diagnosis, health monitoring, machine learning, support vector machines, wind turbine
Group of research
CoDAlab - Control, Dynamics and Applications

Participants