Articles | Volume 11, issue 10
https://doi.org/10.5194/wes-11-4041-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
The influence of flow-field representation on electrical power and fatigue-load surrogate modelling in offshore wind farms
Cited articles
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V. F., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gülçehre, Ç., Song, H. F., Ballard, A. J., Gilmer, J., Dahl, G. E., Vaswani, A., Allen, K. R., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli, P., Botvinick, M. M., Vinyals, O., Li, Y., and Pascanu, R.: Relational inductive biases, deep learning, and graph networks, CoRR, arXiv, abs/1806.01261, https://doi.org/10.48550/arXiv.1806.01261, 2018. a
Bronstein, M. M., Bruna, J., Cohen, T., and Velickovic, P.: Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, CoRR, arXiv, abs/2104.13478, https://doi.org/10.48550/arXiv.2104.13478, 2021. a
Dimitrov, N., Kelly, M. C., Vignaroli, A., and Berg, J.: From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases, Wind Energ. Sci., 3, 767–790, https://doi.org/10.5194/wes-3-767-2018, 2018. a
Duthé, G., Santos, F. d. N., Abdallah, I., Réthore, P.-É., Weijtjens, W., Chatzi, E., and Devriendt, C.: Local flow and loads estimation on wake-affected wind turbines using graph neural networks and PyWake, J. Phys. Conf. Ser., 2505, 012014, https://doi.org/10.1088/1742-6596/2505/1/012014, 2023. a
Gasparis, G., Lio, W. H., and Meng, F.: Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm, Energies, 13, https://doi.org/10.3390/en13236360, 2020. a, b, c, d