Articles | Volume 9, issue 4
https://doi.org/10.5194/wes-9-799-2024
© 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-9-799-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Apr 2024
Research article |
| 05 Apr 2024
| 05 Apr 2024
Sensitivity of fatigue reliability in wind turbines: effects of design turbulence and the Wöhler exponent
Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark
Paul Veers
National Renewable Energy Laboratory (NREL), Golden, CO, USA
Jennifer Rinker
Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark
Katherine Dykes
Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark
Related authors
Shadan Mozafari, Jennifer Rinker, Paul Veers, and Katherine Dykes
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-68, https://doi.org/10.5194/wes-2024-68, 2024
Revised manuscript under review for WES
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The study clarifies the use of probabilistic extrapolation of short/mid-term data for long-term site-specific fatigue assessments. In addition, it assesses the accountability of the Frandsen model in the Lillgrund wind farm as an example of compact layout.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
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Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Shadan Mozafari, Jennifer Rinker, Paul Veers, and Katherine Dykes
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-68, https://doi.org/10.5194/wes-2024-68, 2024
Revised manuscript under review for WES
Short summary
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The study clarifies the use of probabilistic extrapolation of short/mid-term data for long-term site-specific fatigue assessments. In addition, it assesses the accountability of the Frandsen model in the Lillgrund wind farm as an example of compact layout.
Amalia Ida Hietanen, Thor Heine Snedker, Katherine Dykes, and Ilmas Bayati
Wind Energ. Sci., 9, 417–438, https://doi.org/10.5194/wes-9-417-2024, https://doi.org/10.5194/wes-9-417-2024, 2024
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The layout of a floating offshore wind farm was optimized to maximize the relative net present value (NPV). By modeling power generation, losses, inter-array cables, anchors and operational costs, an increase of EUR 34.5 million in relative NPV compared to grid-based layouts was achieved. A sensitivity analysis was conducted to examine the impact of economic factors, providing valuable insights. This study contributes to enhancing the efficiency and cost-effectiveness of floating wind farms.
Lena Kitzing, David Rudolph, Sophie Nyborg, Helena Solman, Tom Cronin, Gundula Hübner, Elizabeth Gill, Katherine Dykes, Suzanne Tegen, and Julia Kirch Kirkegaard
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-174, https://doi.org/10.5194/wes-2023-174, 2024
Preprint withdrawn
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Social aspects are gaining traction in wind energy. A recent publication by Kirkegaard et al. lays out social grand challenges. We discuss them for a more technologically focused audience. We describe the role of social sciences in wind energy research, showing insights, topics, and value-added for public engagement and planning, just ownership and value-based design. We reflect how social and technical sciences can jointly advance wind energy research into a new interdisciplinary era.
Maaike Sickler, Bart Ummels, Michiel Zaaijer, Roland Schmehl, and Katherine Dykes
Wind Energ. Sci., 8, 1225–1233, https://doi.org/10.5194/wes-8-1225-2023, https://doi.org/10.5194/wes-8-1225-2023, 2023
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This paper investigates the effect of wind farm layout on the performance of offshore wind farms. A regular farm layout is compared to optimised irregular layouts. The irregular layouts have higher annual energy production, and the power production is less sensitive to wind direction. However, turbine towers require thicker walls to counteract increased fatigue due to increased turbulence levels in the farm. The study shows that layout optimisation can be used to maintain high-yield performance.
Paul Veers, Carlo L. Bottasso, Lance Manuel, Jonathan Naughton, Lucy Pao, Joshua Paquette, Amy Robertson, Michael Robinson, Shreyas Ananthan, Thanasis Barlas, Alessandro Bianchini, Henrik Bredmose, Sergio González Horcas, Jonathan Keller, Helge Aagaard Madsen, James Manwell, Patrick Moriarty, Stephen Nolet, and Jennifer Rinker
Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, https://doi.org/10.5194/wes-8-1071-2023, 2023
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Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
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We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
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Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
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We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Julian Quick, Jennifer King, Ryan N. King, Peter E. Hamlington, and Katherine Dykes
Wind Energ. Sci., 5, 413–426, https://doi.org/10.5194/wes-5-413-2020, https://doi.org/10.5194/wes-5-413-2020, 2020
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We investigate the trade-offs in optimization of wake steering strategies, where upstream turbines are positioned to deflect wakes away from downstream turbines, with a probabilistic perspective. We identify inputs that are sensitive to uncertainty and demonstrate a realistic optimization under uncertainty for a wind power plant control strategy. Designing explicitly around uncertainty yielded control strategies that were generally less aggressive and more robust to the uncertain input.
Peter Graf, Katherine Dykes, Rick Damiani, Jason Jonkman, and Paul Veers
Wind Energ. Sci., 3, 475–487, https://doi.org/10.5194/wes-3-475-2018, https://doi.org/10.5194/wes-3-475-2018, 2018
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Current approaches to wind turbine extreme load estimation are insufficient to routinely and reliably make required estimates over 50-year return periods. Our work hybridizes the two main approaches and casts the problem as stochastic optimization. However, the extreme variability in turbine response implies even an optimal sampling strategy needs unrealistic computing resources. We therefore conclude that further improvement requires better understanding of the underlying causes of loads.
Related subject area
Thematic area: Materials and operation | Topic: Operation and maintenance, condition monitoring, reliability
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Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen
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Innes Murdo Black, Moritz Werther Häckell, and Athanasios Kolios
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-93, https://doi.org/10.5194/wes-2022-93, 2022
Revised manuscript accepted for WES
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Edward Hart, Elisha de Mello, and Rob Dwyer-Joyce
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Rafael Abreu, Daniel Peter, and Christine Thomas
Wind Energ. Sci., 7, 1227–1239, https://doi.org/10.5194/wes-7-1227-2022, https://doi.org/10.5194/wes-7-1227-2022, 2022
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Clemens Jonscher, Benedikt Hofmeister, Tanja Grießmann, and Raimund Rolfes
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This work presents a method to use low-noise IEPE sensors in the low-frequency range down to 0.05 Hz. In order to achieve phase and amplitude accuracy with this type of sensor in the low-frequency range, a new calibration procedure for this frequency range was developed. The calibration enables the use of the low-noise IEPE sensors for large structures, such as wind turbines. The calibrated sensors can be used for wind turbine monitoring, such as fatigue monitoring.
Cited articles
Bacharoudis, K. C., Antoniou, A., and Lekou, D. J.: Measurement uncertainty of fatigue properties and its effect on the wind turbine blade reliability level, in: Proceedings EWEA, Paris, France, 17–20 November 2015, EWEA, Corpus ID: 52970967, 2015. a
Basquin, O. H.: The Exponential Law of Endurance Tests, in: ASTM, 625–630, Atlantic city, New Jersey, 28 June 28–2 July 1910, Corpus ID: 222450133 1910. a
Burton, T., Jenkins, N., Sharpe, D., and Bossanyi, E.: Wind energy handbook, John Wiley & Sons, https://doi.org/10.1002/9781119992714, 2011. a
Choi, S., Canfield, R. A., and Grandhi, R. V.: Reliability-Based Structural Design, Springer, https://doi.org/10.1007/978-1-84628-445-8, 2007. a
Dimitrov, N. K.: Structural reliability of wind turbine blades: Design methods and evaluation, PhD, Technical University of Denmark, 2013. a
Dimitrov, N. K., Natarajan, A., and Mann, J.: Effects of normal and extreme turbulence spectral parameters on wind turbine loads, Renew. Energ., 101, 1180–1193, https://doi.org/10.1016/j.renene.2016.10.001, 2017. a, b
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
Emeis, S.: Current issues in wind energy meteorology, Meteorol. Appl., 21, 803–819, 2014. a
Ernst, B. and Seume, J. R.: Investigation of site-specific wind field parameters and their effect on loads of offshore wind turbines, Energies, 5, 3835–3855, https://doi.org/10.1002/met.1472, 2012. a
Gulvanessian, H., Calgaro, J., and Holický, M.: Designer's Guide to EN 1990: Eurocode: Basis of Structural Design, Thomas Telford, https://doi.org/10.1680/dgte.30114, 2002. a
Hansen, K. S. and Larsen, G. C.: Characterising turbulence intensity for fatigue load analysis of wind turbines, Wind Engineering, 29, 319–329, https://doi.org/10.1260/030952405774857897, 2005. a
Ishihara, T., Yamaguchi, A., and Sarwar, M. W.: A study of the normal turbulence model in IEC 61400-1, Wind Engineering, 36, 759–765, https://doi.org/10.1260/0309-524X.36.6.759, 2012. a
Kececioglu, D.: Reliability engineering handbook, vol. 1, DEStech Publications, Inc, ISBN13: 978-1-932078-00-8, 2002. a
Larsen, G. C.: Offshore fatigue design turbulence, Wind Energy, 4, 107–120, https://doi.org/10.1002/we.49, 2001. a
Larsen, T. J. and Hansen, A. M.: How 2 HAWC2, the user's manual, ver. 12.9, Risø National Laboratory, Technical University of Denmark Roskilde, Denmark, ISSN 0106-2840, ISBN 978-87-550-3583-6, 2021. a
Le, X. and Peterson, M. L.: A method for fatigue-based reliability when the loading of a component is unknown, Int. J. Fatigue, 21, 603–610, https://doi.org/10.1016/S0142-1123(99)00016-X, 1999. a
Madsen, H. O., Krenk, S., and Lind, N. C.: Methods of structural safety, Courier Corporation, ISBN 0486445976, 9780486445977, 2006. a
Mann, J.: Wind field simulation, Probabilist. Eng. Mech., 13, 269–282, https://doi.org/10.1016/S0266-8920(97)00036-2, 1998. a
Márquez-Domínguez, S. and Sørensen, J. D.: Fatigue reliability and calibration of fatigue design factors for offshore wind turbines, Energies, 5, 1816–1834, https://doi.org/10.3390/en5061816, 2012. a
Melchers, R. E. and Beck, A. T. (Eds.): Structural reliability analysis and prediction, John Wiley & Sons, https://doi.org/10.1002/9781119266105, 2018. a, b
Mikkelsen, L. P.: The fatigue damage evolution in the load-carrying composite laminates of wind turbine blades, in: Fatigue Life Prediction of Composites and Composite Structures, Elsevier, 569–603, https://doi.org/10.1016/B978-0-08-102575-8.00016-4, 2020. a
Miner, M. A.: Cumulative damage in fatigue, J. Appl. Mech.-T. ASME, 12, A159–A164, https://doi.org/10.1115/1.4009458, 1945. a
Mortensen, U. A., Rasmussen, S., Mikkelsen, L. P., Fraisse, A., and Andersen, T. L.: The impact of the fiber volume fraction on the fatigue on performance of glass fiber composites, Compos. Part A-Appl. S., 169, 107493, https://doi.org/10.1016/j.compositesa.2023.107493, 2023. a
Murcia, J. P., Réthoré, P., Dimitrov, N. K., Natarajan, A., Sørensen, J. D., Graf, P., and Kim, T.: Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates, Renew. Energ., 119, 910–922, https://doi.org/10.1016/j.renene.2017.07.070, 2018. a
Øistad, I. S.: Analysis of the Turbulence Intensity at Skipheia Measurement Station, Master’s thesis, NTNU, 2015. a
Palmgren, A.: Die lebensdauer von kugellargern, Z. Ver. Dtsch. Ing., 68, 339–341, 1924. a
Rackwitz, R.: Aspects of structural reliability: in honor of R. Rackwitz, Herbert Utz Verlag, ISBN-10: 3831607524, ISBN-13: 978-3831607525, 2007. a
Rackwitz, R. and Fiessler, B.: Non-normal vectors in structural reliability, Sonderforschungsbereich 96, https://mediatum.ub.tum.de/doc/1634453/1634453.pdf (last access: 23 March 2024), 1978. a
Ren, G., Liu, J., Wan, J., Li, F., Guo, Y., and Yu, D.: The analysis of turbulence intensity based on wind speed data in onshore wind farms, Renew. Energ., 123, 756–766, https://doi.org/10.1016/j.renene.2018.02.080, 2018. a
Robertson, A. N., Shaler, K., Sethuraman, L., and Jonkman, J.: Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads, Wind Energ. Sci., 4, 479–513, https://doi.org/10.5194/wes-4-479-2019, 2019. a
Rognin, F., Abdi, F., Kunc, V., Lee, M., and Nikbin, K.: Probabilistic methods in predicting damage under multi-stage fatigue of composites using load block sequences, Procedia Engineer., 1, 55–58, https://doi.org/10.1016/j.proeng.2009.06.015, 2009. a
Ronold, K. O., Wedel-Heinen, J., and Christensen, C. J.: Reliability-based fatigue design of wind-turbine rotor blades, Eng. Struct., 21, 1101–1114, https://doi.org/10.1016/S0141-0296(98)00048-0, 1999. a
Schaff, J. R. and Davidson, B. D.: A strength-based wearout model for predicting the life of composite structures, ASTM special technical publication, 1285, 179–200, https://doi.org/10.1520/STP19928S, 1997. a
ShadanMozafari: ShadanMozafari/Paper_WES_reliability: wes-2023-47, Version wes-2023-47, Zenodo [code], https://doi.org/10.5281/zenodo.10842326, 2024. a
Slot, R. M., Sørensen, J. D., Svenningsen, L., Moser, W., and Thøgersen, M. L.: Effective turbulence and its implications in wind turbine fatigue assessment, Wind Energy, 22, 1699–1715, https://doi.org/10.1002/we.2397, 2019. a
Søndergaard, A. d. L. and Jóhannsson, H. P.: Assessment of fatigue and extreme loading of wind turbines, MS thesis, Aalborg University, oai:pure.atira.dk:studentproject/03da29f3-26b2-449d-aa5f-36924a175d76, 2016. a
Sørensen, J. D.: Reliability assessment of wind turbines in Safety, Reliability and Risk Anaysis: Beyond the Horizon, in: Proccedings of the European safety and reliability conference, Esrel 2013, Amsterdam, The Netherlands, 29 September–2 October 2013, ISBN (Print) 978-1-138-00123-7, ISBN (Electronic) 978-315-81559-6, 2015 a, b
Stensgaard, H. T., Svenningsen, L., Moser, W., Sørensen, J. D., and Thøgersen, M. L.: Wind climate parameters for wind turbine fatigue load assessment, J. Sol. Energ.-T. ASME, 138, 031010, https://doi.org/10.1115/1.4033111, 2016a. a, b, c
Stensgaard, T. H., Svenningsen, L., Sørensen, J. D., Moser, W., and Thøgersen, M. L.: Uncertainty in wind climate parameters and their influence on wind turbine fatigue loads, Renew. Energ., 90, 352–361, https://doi.org/10.1016/j.renene.2016.01.010, 2016b. a, b
Toft, H. S. and Sørensen, J. D.: Reliability-based design of wind turbine blades, Struct. Saf., 33, 333–342, https://doi.org/10.1016/j.strusafe.2011.05.003, 2011. a, b, c
Toft, H. S., Branner, K., Sørensen, J. D., and Berring, P.: Reliability-based calibration of partial safety factors for wind turbine blades, Brussels, Belgium, March 2011, EWEA, ISBN: 9781618399915, 2011. a
Tsugawa, Y., Sakamoto, N., Kawasaki, S., Arakawa, C., and Iida, M.: Analysis of Wind Turbine Fatigue Loads for Proper Estimation of Offshore Turbulence Intensity, EWEA Offshore 2015, Copenhagen, 10–12 March 2015, EWEA (European academy of wind energy), POID:008, 2015. a
Türk, M. and Emeis, S.: The dependence of offshore turbulence intensity on wind speed, J. Wind Eng. Ind. Aerod., 98, 466–471, https://doi.org/10.1016/j.jweia.2010.02.005, 2010. a
Veers, P. S.: Fatigue reliability of wind turbine components, Sandia National Laboratories, Report No.: SAND-90-2538C, 1990. a
Veers, P. S.: Statistical considerations in fatigue, Journal of Fatigue and Fracture, 19, 295–302, https://doi.org/10.31399/asm.hb.v19.a0002369, 1996. a, b, c
Velarde, J., Mankar, A., Kramhøft, C., and Sørensen, J. D.: Probabilistic calibration of fatigue safety factors for offshore wind turbine concrete structures, Eng. Struct., 222, 111090, https://doi.org/10.1016/j.engstruct.2020.111090, 2020. a
Veldkamp, D.: A probabilistic approach to wind turbine fatigue design, PhD thesis, Technical University of Delft, DUWIND Delft University Wind Energy Research Institute, ISBN-10: 90-76468-12-5, ISBN-13: 978-90-76468-12-9, 2006. a
Wang, H., Barthelmie, R. J., Pryor, S. C., and Kim, H. G.: A new turbulence model for offshore wind turbine standards, Wind Energy, 17, 1587–1604, https://doi.org/10.1002/we.1654, 2014. a, b
Yanan, C., Zhenand, S., and Shunhe, L.: Experimental study on fatigue behavior of composite laminate, Acta Aeronautica et Astronautica Sinica, 12, B643–B646, 1991. a
Zaccone, M.: Failure analysis of helical springs under compressor start/stop conditions, Practical Failure Analysis, 1, 51–62, https://doi.org/10.1007/BF02715198, 2001. a
Short summary
Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how to model the turbulence in normal wind conditions in the design phase. To address such debates, we study the fatigue load distribution and reliability following different models of the International Electrotechnical Commission 61400-1 standard. The results show the lesser importance of load uncertainty due to turbulence distribution compared to the uncertainty of material resistance and Miner’s rule.
Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how...
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