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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2020-79
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2020-79
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 11 May 2020

Submitted as: research article | 11 May 2020

Review status
A revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

A surrogate model approach for associating wind farm load variations with turbine failures

Laura Schröder, Nikolay Krasimirov Dimitrov, and David Robert Verelst Laura Schröder et al.
  • DTU Wind Energy, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark

Abstract. In order to ensure structural reliability, wind turbine design is typically based on the assumption of gradual degradation of material properties (fatigue loading). However, the relation between the wake-induced load exposure of turbines and the reliability of their major components has not been sufficiently well defined and demonstrated. This study suggests a methodology that makes it possible to correlate loads with reliability of turbines in wind farms in a computationally efficient way by combining physical modeling with machine learning. It can be used for estimating the current health state of a turbine and enables a more precise prediction of the load budget, i.e. the effect of load-induced degradation and faults on the operating costs of wind farms. The suggested approach is demonstrated on an offshore wind farm for comparing performance, loads and lifetime estimations against recorded main bearing failures from maintenance reports. The validation of the estimated power against the 10-min SCADA power signals shows that the surrogate model is able to capture the power performance relatively well with a 1.5 % average error in the prediction of the Annual Energy Production (AEP). It is found that turbines positioned at the border of the wind farm with a higher expected AEP are estimated to experience earlier main bearing failures.

Laura Schröder et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Laura Schröder et al.

Data sets

HAWC2 simulations for creating a wind farm surrogate model of a 5MW offshore wind turbine L. Schröder https://doi.org/10.11583/DTU.12245978

Laura Schröder et al.

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Latest update: 07 Jul 2020
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Short summary
We suggest a methodology for correlating loads with component reliability of turbines in wind farms by combining physical modeling with machine learning. The suggested approach is demonstrated on an offshore wind farm for comparing performance, loads and lifetime estimations against recorded main bearing failures from maintenance reports. It is found that turbines positioned at the border of the wind farm with a higher expected AEP are estimated to experience earlier main bearing failures.
We suggest a methodology for correlating loads with component reliability of turbines in wind...
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