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Wind turbine multi-fault detection and classification based on SCADA data

Author
Vidal, Y.; Pozo, F.; Tutivén, C.
Type of activity
Journal article
Journal
Energies
Date of publication
2018
Volume
11
Number
11
First page
1
Last page
18
DOI
https://doi.org/10.3390/en11113018 Open in new window
Project funding
2017 SGR 388: Control, Modelitzacio´, Identificacio´ i Aplicacions (CoDAlab)
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/2117/123827 Open in new window
URL
http://www.mdpi.com/1996-1073/11/11/3018/htm Open in new window
Abstract
Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a dat...
Citation
Vidal, Y., Pozo, F., Tutivén, C. Wind turbine multi-fault detection and classification based on SCADA data. "Energies", 2018, vol. 11, núm. 11, p. 1-18.
Keywords
(Fatigue, Aerodynamics, Structures and Turbulence) FAST code, fault classification, fault detection, fault diagnosis, principal component analysis, support vector machines, wind turbine
Group of research
CoDAlab - Control, Dynamics and Applications

Participants