Articles | Volume 5, issue 3
Research article
17 Aug 2020
Research article |  | 17 Aug 2020

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

Laura Schröder, Nikolay Krasimirov Dimitrov, and David Robert Verelst


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 Laura Schröder on behalf of the Authors (27 Jun 2020)  Author's response   Manuscript 
ED: Publish as is (04 Jul 2020) by Athanasios Kolios
ED: Publish as is (05 Jul 2020) by Gerard J.W. van Bussel (Chief editor)
AR by Laura Schröder on behalf of the Authors (06 Jul 2020)
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.
Final-revised paper