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

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Cited articles

Bangalore, P. and Patriksson, M.: Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines, Renew. Energ., 115, 521–532, 2018. a
Calderon, J. F. G.: Electromechanical drivetrain simulation, DTU Wind Energy, Roskilde, Denmark, 2015. a, b
Colone, L., Natarajan, A., and Dimitrov, N.: Impact of turbulence induced loads and wave kinematic models on fatigue reliability estimates of offshore wind turbine monopiles, Ocean Eng., 155, 295–309, 2018. a, b
Dimitrov, N.: Surrogate models for parameterized representation of wake-induced loads in wind farms, Wind Energy, 22, 1371–1389,, 2019. a, b, c, d, e, f, g, h, i, j
Dimitrov, N. and Natarajan, A.: From SCADA to lifetime assessment and performance optimization: how to use models and machine learning to extract useful insights from limited data, J. Phys. Conf. Ser., 1222, 012032,, 2019. a
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