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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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Marcel Schedat on behalf of the Authors (20 Apr 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (05 Jul 2020) by Gerard J.W. van Bussel
RR by Anonymous Referee #1 (23 Jul 2020)
RR by Anonymous Referee #2 (11 Aug 2020)
ED: Publish subject to minor revisions (review by editor) (30 Nov 2020) by Gerard J.W. van Bussel
AR by Marcel Schedat on behalf of the Authors (31 Jan 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (05 Feb 2021) by Gerard J.W. van Bussel
ED: Publish as is (11 Mar 2021) by Jakob Mann(Chief Editor)
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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.