scholarly journals Structural health and prognostics management for offshore wind turbines :

2013 ◽  
Author(s):  
Noah J. Myrent ◽  
Joshua F. Kusnick ◽  
Natalie C. Barrett ◽  
Douglas E. Adams ◽  
Daniel Griffith
Keyword(s):  
Wind Turbines ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Structural Health

scholarly journals Structural Health Monitoring for Jacket-Type Offshore Wind Turbines: Experimental Proof of Concept

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1835 ◽  
Author(s):  
Yolanda Vidal ◽  
Gabriela Aquino ◽  
Francesc Pozo ◽  
José Eligio Moisés Gutiérrez-Arias
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Vibration Response ◽  
Offshore Wind ◽  
Support Vector ◽  
Soil Conditions ◽  
Small Scale ◽  
Offshore Wind Turbines ◽  
Structural Health

Structural health monitoring for offshore wind turbines is imperative. Offshore wind energy is progressively attained at greater water depths, beyond 30 m, where jacket foundations are presently the best solution to cope with the harsh environment (extreme sites with poor soil conditions). Structural integrity is of key importance in these underwater structures. In this work, a methodology for the diagnosis of structural damage in jacket-type foundations is stated. The method is based on the criterion that any damage or structural change produces variations in the vibrational response of the structure. Most studies in this area are, primarily, focused on the case of measurable input excitation and vibration response signals. Nevertheless, in this paper it is assumed that the only available excitation, the wind, is not measurable. Therefore, using vibration-response-only accelerometer information, a data-driven approach is developed following the next steps: (i) the wind is simulated as a Gaussian white noise and the accelerometer data are collected; (ii) the data are pre-processed using group-reshape and column-scaling; (iii) principal component analysis is used for both linear dimensionality reduction and feature extraction; finally, (iv) two different machine-learning algorithms, k nearest neighbor (k-NN) and quadratic-kernel support vector machine (SVM), are tested as classifiers. The overall accuracy is estimated by 5-fold cross-validation. The proposed approach is experimentally validated in a laboratory small-scale structure. The results manifest the reliability of the stated fault diagnosis method being the best performance given by the SVM classifier.

scholarly journals Structural health and prognostics management for offshore wind turbines :

2012 ◽  
Author(s):  
Daniel Griffith ◽  
Brian Ray Resor ◽  
Jonathan Randall White ◽  
Joshua A. Paquette ◽  
Nathanael C. Yoder
Keyword(s):  
Wind Turbines ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Structural Health

scholarly journals Aspects of structural health and condition monitoring of offshore wind turbines

2015 ◽  
Vol 373 (2035) ◽  
pp. 20140075 ◽  
Author(s):  
I. Antoniadou ◽  
N. Dervilis ◽  
E. Papatheou ◽  
A. E. Maguire ◽  
K. Worden
Keyword(s):  
Signal Processing ◽  
Damage Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Successful Implementation ◽  
Offshore Wind Turbines ◽  
Structural Health ◽  
The Past

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

scholarly journals Vibration based structural health monitoring of the substructures of five offshore wind turbines

2017 ◽  
Vol 199 ◽  
pp. 2294-2299 ◽  
Author(s):  
Wout Weijtjens ◽  
Tim Verbelen ◽  
Emanuele Capello ◽  
Christof Devriendt
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Structural Health
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