Articles | Volume 10, issue 1
https://doi.org/10.5194/wes-10-227-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-227-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Challenges in detecting wind turbine power loss: the effects of blade erosion, turbulence, and time averaging
Vattenfall, Amerigo-Vespucci-Platz 2, 20457 Hamburg, Germany
Christian Bak
DTU Wind and Energy Systems, Frederiksborgvej 399, 4000 Roskilde, Denmark
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Tahir H. Malik and Christian Bak
Wind Energ. Sci., 10, 269–291, https://doi.org/10.5194/wes-10-269-2025, https://doi.org/10.5194/wes-10-269-2025, 2025
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This research integrates custom sensors into wind turbine simulation models for improved performance monitoring utilising a turbine performance integral (TPI) method developed here. Real-world data validation demonstrates that appropriate sensor selection improves wind turbine performance monitoring. This approach addresses the need for precise performance assessments in the evolving wind energy sector, ultimately promoting sustainability and efficiency.
Tahir H. Malik and Christian Bak
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We explore the effect of blade modifications on offshore wind turbines' performance through a detailed analysis of 12 turbines over 12 years. Introducing the turbine performance integral method, which utilises time-series decomposition that combines various data sources, we uncover how blade wear, repairs and software updates impact efficiency. The findings offer valuable insights into improving wind turbine operations, contributing to the enhancement of renewable energy technologies.
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We verify wake effects between two turbines in complex terrain using Supervisory Control and Data Acquisition data. By identifying “wake conditions” and “no-wake conditions” by the blade pitch angle of the upstream wind turbine, we evaluate wake effects on wind speed, turbulent intensity, and power output. Results show that flow downhill has a significant impact on wake effects compared to flow uphill. The method offers a practical alternative to field measurements in complex terrain.
Tahir H. Malik and Christian Bak
Wind Energ. Sci., 10, 269–291, https://doi.org/10.5194/wes-10-269-2025, https://doi.org/10.5194/wes-10-269-2025, 2025
Short summary
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This research integrates custom sensors into wind turbine simulation models for improved performance monitoring utilising a turbine performance integral (TPI) method developed here. Real-world data validation demonstrates that appropriate sensor selection improves wind turbine performance monitoring. This approach addresses the need for precise performance assessments in the evolving wind energy sector, ultimately promoting sustainability and efficiency.
Tahir H. Malik and Christian Bak
Wind Energ. Sci., 9, 2017–2037, https://doi.org/10.5194/wes-9-2017-2024, https://doi.org/10.5194/wes-9-2017-2024, 2024
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We explore the effect of blade modifications on offshore wind turbines' performance through a detailed analysis of 12 turbines over 12 years. Introducing the turbine performance integral method, which utilises time-series decomposition that combines various data sources, we uncover how blade wear, repairs and software updates impact efficiency. The findings offer valuable insights into improving wind turbine operations, contributing to the enhancement of renewable energy technologies.
Kenneth Loenbaek, Christian Bak, Jens I. Madsen, and Michael McWilliam
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We present a model for assessing the aerodynamic performance of a wind turbine rotor through a different parametrization of the classical blade element momentum model. The model establishes an analytical relationship between the loading in the flow direction and the power along the rotor span. The main benefit of the model is the ease with which it can be applied for rotor optimization and especially load constraint power optimization.
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Wind Energ. Sci., 6, 917–933, https://doi.org/10.5194/wes-6-917-2021, https://doi.org/10.5194/wes-6-917-2021, 2021
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A novel wind turbine rotor optimization methodology is presented. Using an assumption of radial independence it is possible to obtain the Pareto-optimal relationship between power and loads through the use of KKT multipliers, leaving an optimization problem that can be solved at each radial station independently. Combining it with a simple cost function it is possible to analytically solve for the optimal power per cost with given inputs for the aerodynamics and the cost function.
Cited articles
Abolude, A. T. and Zhou, W.: Assessment and Performance Evaluation of a Wind Turbine Power Output, Energies, 11, 992, https://doi.org/10.3390/en11081992, 2018. a
Badihi, H., Zhang, Y., Jiang, B., Pillay, P., and Rakheja, S.: A comprehensive review on signal-based and model-based condition monitoring of wind turbines: Fault diagnosis and lifetime prognosis, P. IEEE, 110, 754–806, https://doi.org/10.1109/JPROC.2022.3171691, 2022. a
Bak, C.: Aerodynamic design of wind turbine rotors, Advances in wind turbine blade design and materials, Second edition, edited by: Brœndsted, P., Nijssen, R., and Goutianos, S., Woodhead Publishing, Elsevier, https://doi.org/10.1016/B978-0-08-103007-3.00001-X, 2023. a
Bak, C., Zahle, F., Bitsche, R., Kim, T., Yde, A., Henriksen, L., Hansen, M., Blasques, J., Gaunaa, M., and Natarajan, A.: The DTU 10-MW Reference Wind Turbine, danish Wind Power Research 2013, 27–28 May 2013, https://orbit.dtu.dk/en/publications/the-dtu-10-mw-reference-wind-turbine (last access: 27 March 2024), 2013. a
Bak, C., Skrzypiński, W., Gaunaa, M., Villanueva, H., Brønnum, N. F., and Kruse, E. K.: Full scale wind turbine test of vortex generators mounted on the entire blade, J. Phys. Conf. Ser., 753, 022001, https://doi.org/10.1088/1742-6596/753/2/022001, 2016. a
Bak, C., Forsting, A. M., and Sorensen, N. N.: The influence of leading edge roughness, rotor control and wind climate on the loss in energy production, J. Phys. Conf. Ser., 1618, 052050, https://doi.org/10.1088/1742-6596/1618/5/052050, 2020. a
Bak, C., Olsen, A., Forsting, A., Bjerge, M., Handberg, M., and Shkalov, H.: Wind tunnel test of airfoil with erosion and leading edge protection, J. Phys. Conf. Ser., 2507, https://doi.org/10.1088/1742-6596/2507/1/012022, 2023. a
Bak, D., Andersen, P., Madsen Aagaard, H., Gaunaa, M., Fuglsang, P., and Bove, S.: Design and verification of airfoils resistant to surface contamination and turbulence intensity, in: Collection of Technical Papers – AIAA Applied Aerodynamics Conference, AIAA 2008–7050, American Institute of Aeronautics and Astronautics, 26th Applied Aerodynamics Conference, 18–21 August 2008, https://doi.org/10.2514/6.2008-7050, 2008. a
Barthelmie, R. J. and Jensen, L.: Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm, Wind Energy, 13, 573–586, https://doi.org/10.1002/we.408, 2010. a
Cappugi, L., Castorrini, A., Bonfiglioli, A., Minisci, E., and Campobasso, M. S.: Machine learning-enabled prediction of wind turbine energy yield losses due to general blade leading edge erosion, Energ. Convers. Manage., 245, 114567, https://doi.org/10.1016/j.enconman.2021.114567, 2021. a, b
Castorrini, A., Ortolani, A., and Campobasso, M. S.: Assessing the progression of wind turbine energy yield losses due to blade erosion by resolving damage geometries from lab tests and field observations, Renew. Energ., 218, 119256, https://doi.org/10.1016/j.renene.2023.119256, 2023. a
Ding, Y., Barber, S., and Hammer, F.: Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements, Frontiers in Energy Research, 10, 1050342, https://doi.org/10.3389/fenrg.2022.1050342, 2022. a
Do, M.-T. and Berthaut-Gerentes, J.: Optimal time step of SCADA data for the power curve of wind turbine, J. Phys. Conf. Ser., 1102, 012025, https://doi.org/10.1088/1742-6596/1102/1/012025, 2018. a
Ehrmann, R. S., Wilcox, B., White, E. B., and Maniaci, D. C.: Effect of surface roughness on wind turbine performance, Tech. rep., Sandia National Lab.(SNL-NM), Albuquerque, NM, United States, https://doi.org/10.2172/1596202, 2017. a
Elliott, D. and Infield, D.: An assessment of the impact of reduced averaging time on small wind turbine power curves, energy capture predictions and turbulence intensity measurements, Wind Energy, 17, 337–342, https://doi.org/10.1002/we.1579, 2012. a
EMD International A/S: WindPRO Software for Wind Energy Analysis, https://www.emd.dk/windpro/ (last access: 1 June 2023), 2023. a
Gaudern, N.: A practical study of the aerodynamic impact of wind turbine blade leading edge erosion, J. Phys. Conf. Ser., 524, 012031, https://doi.org/10.1088/1742-6596/524/1/012031, 2014. a
Gonzalez, E., Stephen, B., Infield, D., and Melero, J. J.: On the use of high-frequency SCADA data for improved wind turbine performance monitoring, J. Phys. Conf. Ser., 926, 012009, https://doi.org/10.1088/1742-6596/926/1/012009, 2017. a
Han, W., Kim, J., and Kim, B.: Effects of contamination and erosion at the leading edge of blade tip airfoils on the annual energy production of wind turbines, Renew. Energ., 115, 817–823, https://doi.org/10.1016/j.renene.2017.09.002, 2018. a
Hansen, M. O.: Aerodynamics of wind turbines, Earthscan, James & James, 8, 14, Earthscan Ltd, ISBN 978-1844074389, 2008. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, https://doi.org/10.2172/947422, 2009. a
Kim, D.-Y., Kim, Y.-H., and Kim, B.-S.: Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear, Energy, 214, 119051, https://doi.org/10.1016/j.energy.2020.119051, 2021. a
Krog Kruse, E., Bak, C., and Olsen, A. S.: Wind tunnel experiments on a NACA 633-418 airfoil with different types of leading edge roughness, Wind Energy, 24, 1263–1274, https://doi.org/10.1002/we.2630, 2021. a, b, c, d
Kruse, E.: A Method for Quantifying Wind Turbine Leading Edge Roughness and its Influence on Energy Production: LER2AEP, Phd, DTU Wind Energy, Roskilde, Denmark, (DTU Wind Energy PhD), https://orbit.dtu.dk/en/publications/a-method-for-quantifying-wind-turbine-leading-edge-roughness-and–2 (last access: 27 March 2024), 2019. a
Malik, T. H. and Bak, C.: Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences, Wind Energ. Sci., 9, 2017–2037, https://doi.org/10.5194/wes-9-2017-2024, 2024. a
Maniaci, D., White, E., Wilcox, B., Langel, C., van Dam, C., and Paquette, J.: Experimental Measurement and CFD Model Development of Thick Wind Turbine Airfoils with Leading Edge Erosion, J. Phys. Conf. Ser.,753, 022013,, https://doi.org/10.1088/1742-6596/753/2/022013, 2016. a
Mann, J.: The spatial structure of neutral atmospheric surface-layer turbulence, J. Fluid Mech., 273, 141–168, 1994. a
OpenAI: ChatGPT: Optimizing Language Models for Dialogue, https://openai.com/chatgpt/ (last access: 28 November 2024), 2024. a
Saint-Drenan, Y.-M., Besseau, R., Jansen, M., Staffell, I., Troccoli, A., Dubus, L., Schmidt, J., Gruber, K., Simões, S. G., and Heier, S.: A parametric model for wind turbine power curves incorporating environmental conditions, Renew. Energ., 157, 754–768, https://doi.org/10.1016/j.renene.2020.04.123, 2020. a, b, c, d
Skrzypinski, W., Gaunaa, M., and Bak, C.: The Effect of Mounting Vortex Generators on the DTU 10MW Reference Wind Turbine Blade, vol. 524, IOP Publishing, 5th International Conference on The Science of Making Torque from Wind 2014, TORQUE 2014, 10–20 June 2014, https://doi.org/10.1088/1742-6596/524/1/012034, 2014. a
St. Martin, C. M., Lundquist, J. K., Clifton, A., Poulos, G. S., and Schreck, S. J.: Wind turbine power production and annual energy production depend on atmospheric stability and turbulence, Wind Energ. Sci., 1, 221–236, https://doi.org/10.5194/wes-1-221-2016, 2016. a
Wagner, R., Courtney, M., Larsen, T. J., and Paulsen, U. S.: Simulation of shear and turbulence impact on wind turbine performance, Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi, https://orbit.dtu.dk/en/publications/simulation-of-shear-and-turbulence-impact-on-wind-turbine-perform (last access: 27 March 2024), 2010. a, b
Wharton, S. and Lundquist, J. K.: Atmospheric stability affects wind turbine power collection, Environ. Res. Lett., 7, 014005, https://doi.org/10.1088/1748-9326/7/1/014005, 2012. a
Yang, W., Tavner, P. J., Crabtree, C. J., Feng, Y., and Qiu, Y.: Wind turbine condition monitoring: technical and commercial challenges, Wind Energy, 17, 673–693, https://doi.org/10.1002/we.1508, 2014. a
Short summary
This study investigates how wind turbine blades damaged by erosion, along with changing wind conditions, affect power output. Even minor blade damage can lead to significant energy losses, especially in turbulent winds. Using simulations, it was discovered that standard power data analysis methods, including time averaging, can hide these losses. This research highlights the need for better blade damage detection and careful wind data analysis to optimise wind farm performance.
This study investigates how wind turbine blades damaged by erosion, along with changing wind...
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