Articles | Volume 6, issue 2
https://doi.org/10.5194/wes-6-539-2021
https://doi.org/10.5194/wes-6-539-2021
Research article
 | 
21 Apr 2021
Research article |  | 21 Apr 2021

Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks

Artur Movsessian, Marcel Schedat, and Torsten Faber

Related subject area

Design methods, reliability and uncertainty modelling
Effectively using multifidelity optimization for wind turbine design
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022,https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling
Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, and Gerard Schepers
Wind Energ. Sci., 7, 759–781, https://doi.org/10.5194/wes-7-759-2022,https://doi.org/10.5194/wes-7-759-2022, 2022
Short summary
Fast yaw optimization for wind plant wake steering using Boolean yaw angles
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022,https://doi.org/10.5194/wes-7-741-2022, 2022
Short summary
A simplified, efficient approach to hybrid wind and solar plant site optimization
Charles Tripp, Darice Guittet, Jennifer King, and Aaron Barker
Wind Energ. Sci., 7, 697–713, https://doi.org/10.5194/wes-7-697-2022,https://doi.org/10.5194/wes-7-697-2022, 2022
Short summary
Influence of wind turbine design parameters on linearized physics-based models in OpenFAST
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022,https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary

Cited articles

Boslaugh, S. and Watters, P. A.: Statistics in a Nutshell by Sarah Boslaugh and Paul Andrew Watters, Copyright © 2008 Sarah Boslaugh, O'Reilly Media, Inc., Sebastopol, CA, USA, 2008. 
Cosack, N.: Fatigue Load Monitoring with Standard Wind Turbine Signals, University of Stuttgart, Stuttgart, available at: https://d-nb.info/1009926721/34 (last access: 15 June 2019), 2010. 
Cosack, N. and Kühn, M.: Ueberwachung von Belastungen an Windenergieanlagen durch Analyse von Standardsignalen.pdf, in: AKIDA Tagungsband, 6. Aachener Kolloquium für Instandhaltung, Diagnose und Anlagenüberwachung, 14–15 November 2006, Aachen, 277–283., 2006. 
Cosack, N. and Kühn, M.: Prognose von Ermüdungslasten an Windenergieanlagen mittels Standardsignalen und neuronaler Netze.pdf, in: DMK 2007 – Dresdner Maschinenelemente Kolloquium: 5 and 6 December 2007, Dresden, 461–476, 2007. 
DNV/Risø: Guidelines for Design of Wind Turbines, 2nd Edn., Jydsk Centraltrykkeri, Denmark, 2002. 
Download
Short summary
The assessment of the structural condition and technical lifetime extension of a wind turbine is challenging due to lack of information for the estimation of fatigue loads. This paper demonstrates the modelling of damage-equivalent loads of the fore–aft bending moments of a wind turbine tower, highlighting the advantage of using the neighbourhood component analysis. This feature selection technique is compared to correlation analysis, stepwise regression, and principal component analysis.
Altmetrics
Final-revised paper
Preprint