Articles | Volume 9, issue 10
https://doi.org/10.5194/wes-9-2017-2024
© Author(s) 2024. 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-9-2017-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences
Vattenfall, Amerigo-Vespucci-Platz 2, 20457, Hamburg, Germany
Christian Bak
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Related authors
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
Short summary
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., 10, 227–243, https://doi.org/10.5194/wes-10-227-2025, https://doi.org/10.5194/wes-10-227-2025, 2025
Short summary
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.
Nanako Sasanuma, Akihiro Honda, Christian Bak, Niels Troldborg, Mac Gaunaa, Morten Nielsen, and Teruhisa Shimada
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-130, https://doi.org/10.5194/wes-2025-130, 2025
Preprint under review for WES
Short summary
Short summary
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
Short summary
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., 10, 227–243, https://doi.org/10.5194/wes-10-227-2025, https://doi.org/10.5194/wes-10-227-2025, 2025
Short summary
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.
Kenneth Loenbaek, Christian Bak, Jens I. Madsen, and Michael McWilliam
Wind Energ. Sci., 6, 903–915, https://doi.org/10.5194/wes-6-903-2021, https://doi.org/10.5194/wes-6-903-2021, 2021
Short summary
Short summary
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.
Kenneth Loenbaek, Christian Bak, and Michael McWilliam
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
Short summary
Short summary
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
Aho, J., Buckspan, A., Laks, J., Fleming, P., Jeong, Y., Dunne, F., Churchfield, M., Pao, L., and Johnson, K.: A tutorial of wind turbine control for supporting grid frequency through active power control, in: 2012 American Control Conference (ACC), 3120–3131, IEEE, https://doi.org/10.1109/ACC.2012.6315180, 2012. a, b
Albers, A.: Relative and integral wind turbine power performance evaluation, in: Proceedings of the 2012 European Wind Energy Conference & Exhibition, 22–25, https://www.researchgate.net/publication/242780574_Relative_and_Integral_Wind_Turbine_Power_Performance_Evaluation (last access: 1 August 2023), 2012. a
Anderson, T. K., Nelson, M. I., Kitikoon, P., Swenson, S. L., Korslund, J. A., and Vincent, A. L.: Population dynamics of cocirculating swine influenza A viruses in the United States from 2009 to 2012, Influenza Other Resp., 7, 42–51, 2013. a
Astolfi, D., Byrne, R., and Castellani, F.: Analysis of Wind Turbine Aging through Operation Curves, Energies, 13, 5623, https://doi.org/10.3390/en13215623, 2020. a
Bak, C.: A simple model to predict the energy loss due to leading edge roughness, J. Phys. Conf. Ser., 2265, 032038, https://doi.org/10.1088/1742-6596/2265/3/032038, 2022. 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
Bechtold, B., Fletcher, P., seamusholden, and Gorur-Shandilya, S.: bastibe/Violinplot-Matlab: A Good Starting Point, https://api.semanticscholar.org/CorpusID:244971580 (last access: 8 August 2023), 2021. a
Bolik, S. M.: Modelling and analysis of variable speed wind turbines with induction generator during grid fault, Institut for Energiteknik, Aalborg Universitet, https://vbn.aau.dk/en/publications/modelling-and-analysis-of-variable-speed-wind-turbines-with-induc (last access: 1 August 2023), 2004. a
Butler, S., Ringwood, J., and O'Connor, F.: Exploiting SCADA system data for wind turbine performance monitoring, in: 2013 Conference on Control and Fault-Tolerant Systems (SysTol), Nice, France, 9–11 October 2013, 389–394, https://doi.org/10.1109/SysTol.2013.6693951, 2013. a, b
Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates, 2 edn., https://doi.org/10.4324/9780203771587, 1988. a
Dao, C., Kazemtabrizi, B., and Crabtree, C.: Wind turbine reliability data review and impacts on levelised cost of energy, Wind Energy, 22, 1848–1871, 2019. a
Dao, P. B.: On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines, Appl. Energ., 318, 119209, https://doi.org/10.1016/j.apenergy.2022.119209, 2022. 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, b
Ernst, B., Schmitt, H., and Seume, J. R.: Effect of geometric uncertainties on the aerodynamic characteristic of offshore wind turbine blades, J. Phys. Conf. Ser., 555, 012033, https://doi.org/10.1088/1742-6596/555/1/012033, 2014. a
Farkas, Z.: Considering air density in wind power production, arXiv [preprint], https://doi.org/10.48550/arXiv.1103.2198, 2011. a, b
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.: Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study, Renew. Energ., 131, 841–853, 2019. a
Hafen, R. P., Anderson, D. E., Cleveland, W. S., Maciejewski, R., Ebert, D. S., Abusalah, A., Yakout, M., Ouzzani, M., and Grannis, S. J.: Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts, BMC Med. Inform. Decis., 9, 1–11, 2009. 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, 2018. a
IEC: IEC 61400-12-1: 2017, Wind Energy Generation Systems – Part, 12, https://webstore.iec.ch/en/publication/26603 (last access: 1 August 2023), 2017. a
Katnam, K., Comer, A., Roy, D., Da Silva, L., and Young, T.: Composite repair in wind turbine blades: an overview, J. Adhesion, 91, 113–139, 2015. 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, 2021. a
Leahy, K., Gallagher, C., O’Donovan, P., and O'Sullivan, D. T.: Issues with data quality for wind turbine condition monitoring and reliability analyses, Energies, 12, 201, https://doi.org/10.3390/en12020201, 2019. a, b
Loeven, A. and Bijl, H.: Airfoil analysis with uncertain geometry using the probabilistic collocation method, in: 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 16th AIAA/ASME/AHS Adaptive Structures Conference, 10th AIAA Non-Deterministic Approaches Conference, 9th AIAA Gossamer Spacecraft Forum, 4th AIAA Multidisciplinary Design Optimization Specialists Conference, 2070, https://doi.org/10.2514/6.2008-2070, 2008. a, b
Maniaci, D. C., White, E. B., Wilcox, B., Langel, C. M., van Dam, C., and Paquette, J. A.: 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
Mishnaevsky Jr., L., Hasager, C. B., Bak, C., Tilg, A.-M., Bech, J. I., Rad, S. D., and Fæster, S.: Leading edge erosion of wind turbine blades: Understanding, prevention and protection, Renew. Energ., 169, 953–969, 2021. a
Murphy, P., Lundquist, J. K., and Fleming, P.: How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine, Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, 2020. a
OpenAI: ChatGPT: Optimizing Language Models for Dialogue, https://openai.com/chatgpt/ (last access: 1 May 2024), 2023. a
Pindado, S., Barrero-Gil, A., and Sanz, A.: Cup anemometers’ loss of performance due to ageing processes, and its effect on annual energy production (AEP) estimates, Energies, 5, 1664–1685, 2012. a
Prema, V. and Rao, K. U.: Time series decomposition model for accurate wind speed forecast, Renewables: Wind, Water, and Solar, 2, 1–11, 2015. a
Sanchez-Vazquez, M. J., Nielen, M., Gunn, G. J., and Lewis, F. I.: Using seasonal-trend decomposition based on loess (STL) to explore temporal patterns of pneumonic lesions in finishing pigs slaughtered in England, 2005–2011, Prev. Vet. Med., 104, 65–73, 2012. a
Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality (complete samples), Biometrika, 52, 591–611, 1965. 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
Student: The probable error of a mean, Biometrika, 6, 1–25, 1908. a
Tautz-Weinert, J. and Watson, S. J.: Using SCADA data for wind turbine condition monitoring – a review, IET Renew. Power Gen., 11, 382–394, 2017. a
The MathWorks, Inc.: MATLAB: trenddecomp function, mATLAB version 2023b https://uk.mathworks.com/help/matlab/ref/double.trenddecomp.html (last access: September 2023), 2023. a
van Dijk, M. T., van Wingerden, J.-W., Ashuri, T., Li, Y., and Rotea, M. A.: Yaw-misalignment and its impact on wind turbine loads and wind farm power output, J. Phys. Conf. Ser., 753, 062013, https://doi.org/10.1088/1742-6596/753/6/062013, 2016. a
Verbesselt, J., Hyndman, R., Newnham, G., and Culvenor, D.: Detecting trend and seasonal changes in satellite image time series, Remote Sens. Environ., 114, 106–115, 2010. a
Wan, S., Cheng, L., and Sheng, X.: Effects of yaw error on wind turbine running characteristics based on the equivalent wind speed model, Energies, 8, 6286–6301, 2015. a
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
Wilcoxon, F.: Individual comparisons by ranking methods, Biometrics Bull., 1, 80–83, 1945. a
Xu, L., Ou, Y., Cai, J., Wang, J., Fu, Y., and Bian, X.: Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition, Renew. Energ., 216, 119097, https://doi.org/10.1016/j.renene.2023.119097, 2023. a
Short summary
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.
We explore the effect of blade modifications on offshore wind turbines' performance through a...
Altmetrics
Final-revised paper
Preprint