Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-1007-2020
https://doi.org/10.5194/wes-5-1007-2020
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

Related authors

Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024,https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024,https://doi.org/10.5194/wes-9-1153-2024, 2024
Short summary
Dynamic Modelling and Response of a Power Cable connected to a Floating Wind Turbine
David Robert Verelst, Rasmus Sode Lund, and Jean-Philippe Roques
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-24,https://doi.org/10.5194/wes-2024-24, 2024
Revised manuscript has not been submitted
Short summary
Extreme wind turbine response extrapolation with the Gaussian mixture model
Xiaodong Zhang and Nikolay Dimitrov
Wind Energ. Sci., 8, 1613–1623, https://doi.org/10.5194/wes-8-1613-2023,https://doi.org/10.5194/wes-8-1613-2023, 2023
Short summary
Extreme coherent gusts with direction change – probabilistic model, yaw control, and wind turbine loads
Ásta Hannesdóttir, David R. Verelst, and Albert M. Urbán
Wind Energ. Sci., 8, 231–245, https://doi.org/10.5194/wes-8-231-2023,https://doi.org/10.5194/wes-8-231-2023, 2023
Short summary

Related subject area

Design methods, reliability and uncertainty modelling
Effectively using multifidelity optimization for wind turbine design
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022,https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling
Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, and Gerard Schepers
Wind Energ. Sci., 7, 759–781, https://doi.org/10.5194/wes-7-759-2022,https://doi.org/10.5194/wes-7-759-2022, 2022
Short summary
Fast yaw optimization for wind plant wake steering using Boolean yaw angles
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022,https://doi.org/10.5194/wes-7-741-2022, 2022
Short summary
A simplified, efficient approach to hybrid wind and solar plant site optimization
Charles Tripp, Darice Guittet, Jennifer King, and Aaron Barker
Wind Energ. Sci., 7, 697–713, https://doi.org/10.5194/wes-7-697-2022,https://doi.org/10.5194/wes-7-697-2022, 2022
Short summary
Influence of wind turbine design parameters on linearized physics-based models in OpenFAST
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022,https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary

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, https://doi.org/10.1002/we.2362, 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, https://doi.org/10.1088/1742-6596/1222/1/012032, 2019. a
Download
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.
Altmetrics
Final-revised paper
Preprint