Articles | Volume 3, issue 2
https://doi.org/10.5194/wes-3-767-2018
https://doi.org/10.5194/wes-3-767-2018
Research article
 | 
24 Oct 2018
Research article |  | 24 Oct 2018

From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases

Nikolay Dimitrov, Mark C. Kelly, Andrea Vignaroli, and Jacob Berg

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
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
Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics
Davide Conti, Nikolay Dimitrov, Alfredo Peña, and Thomas Herges
Wind Energ. Sci., 6, 1117–1142, https://doi.org/10.5194/wes-6-1117-2021,https://doi.org/10.5194/wes-6-1117-2021, 2021
Short summary
Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021,https://doi.org/10.5194/wes-6-841-2021, 2021
Short summary
Validation of the dynamic wake meandering model with respect to loads and power production
Inga Reinwardt, Levin Schilling, Dirk Steudel, Nikolay Dimitrov, Peter Dalhoff, and Michael Breuer
Wind Energ. Sci., 6, 441–460, https://doi.org/10.5194/wes-6-441-2021,https://doi.org/10.5194/wes-6-441-2021, 2021
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

Bak, C., Zahle, F., Bitsche, R., Kim, T., Yde, A., Henriksen, L. C., Natarajan, A., and Hansen, M.: Description of the DTU 10 MW reference wind turbine, Tech. Rep. I-0092, Technical University of Denmark, Department of Wind Energy, 2013. a, b, c
Berg, J., Natarajan, A., Mann, J., and Patton, E.: Gaussian vs non-Gaussian turbulence: impact on wind turbine loads, Wind Energy, 19, 1975–1989, 2016. a
Borraccino, A., Schlipf, D., Haizmann, F., and Wagner, R.: Wind field reconstruction from nacelle-mounted lidar short-range measurements, Wind Energ. Sci., 2, 269–283, https://doi.org/10.5194/wes-2-269-2017, 2017. a
Caflisch, R. E.: Monte Carlo and Quasi-Monte Carlo methods, Acta Numer., 7, 1–49, 1998. a
Choe, Y., Byon, E., and Chen, N.: Importance Sampling for Reliability Evaluation With Stochastic Simulation Models, Technometrics, 57, 351–361, https://doi.org/10.1080/00401706.2014.1001523, 2015. a
Download
Short summary
Wind energy site suitability assessment procedures often require estimating the loads a wind turbine will be subject to when installed. The estimation is often time-consuming and requires several iterations. We have developed a procedure for quick and accurate estimation of site-specific wind turbine loads. Our approach employs computationally efficient parametric models that are calibrated to high-fidelity load simulations. The result is a significant reduction in computation efforts.
Altmetrics
Final-revised paper
Preprint