Articles | Volume 6, issue 2
Wind Energ. Sci., 6, 539–554, 2021
https://doi.org/10.5194/wes-6-539-2021
Wind Energ. Sci., 6, 539–554, 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 et al.

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Cited articles

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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. 
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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.