Modular deep learning approach for wind farm power forecasting and wake loss prediction

Stijn Ally, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen

Related authors

Impact of inflow conditions and turbine placement on the performance of offshore wind turbines exceeding 7 MW

Konstantinos Vratsinis, Rebeca Marini, Pieter-Jan Daems, Lukas Pauscher, Jeroen van Beeck, and Jan Helsen

Wind Energ. Sci., 11, 1803–1820, https://doi.org/10.5194/wes-11-1803-2026,https://doi.org/10.5194/wes-11-1803-2026, 2026

Short summary

AI enhanced fault indicators vs. classical bearing monitoring – example results of bearing tests and transferability to wind turbines

Matthias Stammler, Faras Jamil, Xinrun Liu, Jens Jo Matthys, Nikhil Sudhakaran, Cédric Peeters, Asger Bech Abrahamsen, and Jan Helsen

Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-81,https://doi.org/10.5194/wes-2026-81, 2026

Preprint under review for WES

Short summary

Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder

Xavier Chesterman, Ann Nowé, and Jan Helsen

Wind Energ. Sci., 11, 1163–1183, https://doi.org/10.5194/wes-11-1163-2026,https://doi.org/10.5194/wes-11-1163-2026, 2026

Short summary

Impact of Wake Impingement on the Fatigue Loads in the Main Bearings and Blades of Offshore Wind Turbines

Jakob Gebel, Pieter-Jan Daems, Diederik van Binsbergen, Veronica Liverud Krathe, Amir R. Nejad, and Jan Helsen

Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-13,https://doi.org/10.5194/wes-2026-13, 2026

Preprint under review for WES

Short summary

Scalable SCADA-driven failure prediction for offshore wind turbines using autoencoder-based NBM and fleet-median filtering

Ivo Vervlimmeren, Xavier Chesterman, Timothy Verstraeten, Ann Nowé, and Jan Helsen

Wind Energ. Sci., 10, 2615–2637, https://doi.org/10.5194/wes-10-2615-2025,https://doi.org/10.5194/wes-10-2615-2025, 2025

Short summary

Wind turbine drivetrains: state-of-the-art technologies and future development trends

Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen

Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022,https://doi.org/10.5194/wes-7-387-2022, 2022

Short summary

Cited articles

Barthelmie, R. J., Hansen, K., Frandsen, S. T., Rathmann, O., Schepers, J., Schlez, W., Phillips, J., Rados, K., Zervos, A., Politis, E., and Chaviaropoulos, P. K.: Modelling and measuring flow and wind turbine wakes in large wind farms offshore, Wind Energy, 12, 431–444, 2009. a, b

Becker, M., Allaerts, D., and van Wingerden, J. W.: FLORIDyn – A dynamic and flexible framework for real-time wind farm control, J. Phys. Conf. Ser., 2265, 032103, https://doi.org/10.1088/1742-6596/2265/3/032103, 2022. a

Bleeg, J., Purcell, M., Ruisi, R., and Traiger, E.: Wind farm blockage and the consequences of neglecting its impact on energy production, Energies, 11, 1609, https://doi.org/10.3390/en11061609, 2018. a

Boersma, S., Doekemeijer, B. M., Gebraad, P. M., Fleming, P. A., Annoni, J., Scholbrock, A. K., Frederik, J. A., and van Wingerden, J.-W.: A tutorial on control-oriented modeling and control of wind farms, in: 2017 American control conference (ACC), Seattle, WA, USA, 24–26 May 2017, IEEE, 1–18, https://doi.org/10.23919/ACC.2017.7962923, 2017. a, b

Bossanyi, E. and Ruisi, R.: Axial induction controller field test at Sedini wind farm, Wind Energ. Sci., 6, 389–408, https://doi.org/10.5194/wes-6-389-2021, 2021. a