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Wind turbine condition monitoring strategy through multiway PCA and multivariate inference

Author
Pozo, F.; Vidal, Y.; Salgado, Ó.
Type of activity
Journal article
Journal
Energies
Date of publication
2018-03-26
Volume
11
Number
4
First page
749
Last page
768
DOI
https://doi.org/10.3390/en11040749 Open in new window
Project funding
Development and validation of failure detection and design of fault-tolerant control strategies with application in offshore wind energy plants
Development and validation of intelligent monitoring systems, pitch and structural damping control strategies for floating offshore wind turbines
Repository
http://hdl.handle.net/2117/116241 Open in new window
URL
http://www.mdpi.com/1996-1073/11/4/749 Open in new window
Abstract
This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process giv...
Citation
Pozo, F., Vidal, Y., Salgado, Ó. Wind turbine condition monitoring strategy through multiway PCA and multivariate inference. "Energies", 26 Març 2018, vol. 11, núm. 4, p. 749-768.
Keywords
condition monitoring, fault detection, multivariate statistical hypothesis testing, principal component analysis, wind turbine
Group of research
CoDAlab - Control, Dynamics and Applications

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