Articles | Volume 7, issue 5
https://doi.org/10.5194/wes-7-1919-2022
© Author(s) 2022. 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-7-1919-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Sep 2022
Research article |
| 26 Sep 2022
| 26 Sep 2022
Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements
Clemens Hübler
CORRESPONDING AUTHOR
Institute of Structural Analysis, Leibniz University Hannover/ForWind, Appelstr. 9a, 30167 Hanover, Germany
Raimund Rolfes
Institute of Structural Analysis, Leibniz University Hannover/ForWind, Appelstr. 9a, 30167 Hanover, Germany
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Revised manuscript under review for WES
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In this work, for the first time, a Kriging meta-model is detailed analysed to replace the simulation model of an idling offshore wind turbine. It becomes clear that the findings regarding meta-modelling of the idling wind turbine are generally similar to the findings regarding meta-modelling of the same wind turbine in operation. Only for the approximation of the rotor blade root bending moments, two additional input parameters have to be included compared to the same wind turbine in operation.
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In this work, for the first time, a Kriging meta-model is detailed analysed to replace the simulation model of an idling offshore wind turbine. It becomes clear that the findings regarding meta-modelling of the idling wind turbine are generally similar to the findings regarding meta-modelling of the same wind turbine in operation. Only for the approximation of the rotor blade root bending moments, two additional input parameters have to be included compared to the same wind turbine in operation.
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Cited articles
Bartsch, C.: FACT-SHEET alpha ventus, press release of alpha ventus, https://www.alpha-ventus.de/fileadmin/Dateien/publikationen/av_Factsheet_de_2020.pdf, last access: 16 March 2021. a
BMWK – Bundesministeriums für Wirtschaft und Klimaschutz: Entwurf eines Zweiten Gesetzes zur Änderung des Windenergie-auf-See-Gesetzes und anderer Vorschriften, draft bill of the BMWK, https://www.bmwk.de/Redaktion/DE/Downloads/E/entwurf-eines-zweiten-gesetzes-zur-aenderung-des-windenergie (last access: 19 September 2022), 4 March 2022 (in German). a
Bouty, C., Schafhirt, S., Ziegler, L., and Muskulus, M.: Lifetime extension for large offshore wind farms: Is it enough to reassess fatigue for selected design positions?, Energy Proced., 137, 523–530, 2017. a
Cosack, N. and Kühn, M.: Überwachung von Belastungen an Windenergieanlagen durch Analyse von Standardsignalen, AKIDA Tagungsband, 6, 277–283, 2006 (in German). a
Dimitrov, N. and Natarajan, A.: From SCADA to lifetime assessment and performance optimization: how to use models and machine learning to extract useful insights from limited data, J. Phys.-Conf. Ser., 1222, 012032, https://doi.org/10.1088/1742-6596/1222/1/012032, 2019. 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, b
DNV GL AS: Support Structures for Wind Turbines, Standard DNVGL-ST-0126, 4C Offshore: Lowestoft Suffolk, UK, https://www.dnv.com/energy/standards-guidelines/dnv-st-0262-lifetime-extension-of-wind-turbines.html (last access: 19 September 2022), 2016. a
Efron, B.: Bootstrap methods: another look at the jackknife, Ann. Stat., 7, 1–26, 1979. a
European Committee for Standardization: Eurocode 3: Design of Steel Structures – Part 1-9: Fatigue, EN 1993-1-9, European Committee for Standardization: Brussels, Belgium, 2010. a
Goodman, J.: Mechanics applied to engineering, Longmans, Green,
and Co., London, UK, https://archive.org/details/cu31924004025338/mode/2up (last access: 20 September 2022), 1914. a
Henkel, M., Häfele, J., Weijtjens, W., Devriendt, C., Gebhardt, C. G., and Rolfes, R.: Strain estimation for offshore wind turbines with jacket substructures using dual-band modal expansion, Mar. Struct., 71, 102731, https://doi.org/10.1016/j.marstruc.2020.102731, 2020. a, b, c
Hübler, C. and Rolfes, R.: Analysis of the influence of climate change on the fatigue lifetime of offshore wind turbines using imprecise probabilities, Wind Energy, 24, 275–289, 2021. a
Hübler, C., Gebhardt C. G., and Rolfes, R.: Hierarchical four-step global sensitivity analysis of offshore wind turbines based on aeroelastic time domain simulations, Renew. Energ., 111, 878–891, 2017. a
Hübler, C., Weijtjens, W., Gebhardt C. G., Rolfes, R., and Devriendt, C.: Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design, Energies, 12, 603, https://doi.org/10.3390/en12040603, 2019. a
Fraunhofer Institut für Windenergiesysteme (IWES): Liste der Sensoren, technical report, https://www.rave-offshore.de/files/images/Datenarchiv/Datenarchiv_EN/Liste_der_Sensoren.pdf, last access: 16 March 2021. a
Larose, D. T. and Larose, C. D.: Discovering knowledge in data: an introduction to data mining, Vol. 4, John Wiley & Sons, ISBN 978-0-470-90874-7, 2014. a
Long, L., Mai, Q. A., Morato, P. G., Sørensen, J. D., and Thöns, S.: Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures, Renew. Energ., 159, 1036–1046, 2020. a
Mai, Q. A., Weijtjens, W., Devriendt, C., Morato, P. G., Rigo, P., and Sørensen, J. D.: Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data, Mar. Struct., 66, 307–322, 2019. a, b, c, d, e
Movsessian, A., Schedat, M., and Faber, T.: Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks, Wind Energ. Sci., 6, 539–554, https://doi.org/10.5194/wes-6-539-2021, 2021. a
Natarajan, A. and Bergami, L.: Determination of wind farm life consumption in complex terrain using ten-minute SCADA measurements, J. Phys.-Conf. Ser., 1618, 022013, https://doi.org/10.1088/1742-6596/1618/2/022013, 2020. a, b
Nielsen, J. S., Miller-Branovacki, L., and Carriveau, R.: Probabilistic and Risk-Informed Life Extension Assessment of Wind Turbine Structural Components, Energies, 14, 821, https://doi.org/10.3390/en14040821, 2021. a, b
Niesłony, A.: Determination of fragments of multiaxial service loading strongly influencing the fatigue of machine components, Mech. Syst. Signal Pr., 23, 2712–2721, 2009. a
Noppe, N., Weijtjens, W., and Devriendt, C.: Modeling of quasi-static thrust load of wind turbines based on 1 s SCADA data, Wind Energ. Sci., 3, 139–147, https://doi.org/10.5194/wes-3-139-2018, 2018. a
Noppe, N., Hübler, C., Devriendt, C., and Weijtjens, W.: Validated extrapolation of measured damage within an offshore wind farm using instrumented fleet leaders, J. Phys.-Conf. Ser., 1618, 022005, https://doi.org/10.1088/1742-6596/1618/2/022005, 2020. a, b
Petrovska, E., Le Dreff, J. B., Oterkus, S., Thies, P., and McCarthy, E.: Application of Structural Monitoring Data for Fatigue Life Predictions of Monopile-Supported Offshore Wind Turbines, Proceedings of the 39th International Conference on Ocean, Offshore and Arctic Engineering, 3–7 August 2020, Virtual, Online, OMAE2020-18516, https://doi.org/10.1115/OMAE2020-18516, 2020. a, b, c, d
RAVE – Research At Alpha Ventus: Data – Measurements in RAVE, https://www.rave-offshore.de/en/data.html, last access: 19 September 2022. a
Rubert, T., Zorzi, G., Fusiek, G., Niewczas, P., McMillan, D., McAlorum, J., and Perry, M.: Wind turbine lifetime extension decision-making based on structural health monitoring, Renew. Energ., 143, 611–621, 2019. a
Saathoff, M. and Rosemeier, M.: Stress-based assessment of the lifetime extension for wind turbines, J. Phys.-Conf. Ser., 1618, 052057, https://doi.org/10.1088/1742-6596/1618/5/052057, 2020. a
Sadeghi, N., Robbelein, K., D'Antuono, P., Noppe, N., Weijtjens, W., and Devriendt, C.: Fatigue damage calculation of offshore wind turbines’ long-term data considering the low-frequency fatigue dynamics, J. Phys.-Conf. Ser., 2265, 032063, https://doi.org/10.1088/1742-6596/2265/3/032063, 2022. a, b, c
Seifert, J., Vera-Tudela, L., and Kühn, M.: Training requirements of a neural network used for fatigue load estimation of offshore wind turbines, Energy Proced., 137, 315–322, 2017. a
Smith, J. C., Carriveau, R., and Ting, D. S.: Inflow Parameter Effects on Wind Turbine Tower Cyclic Loading, Wind Engineering, 38, 477–488, 2014. a
Smolka, U. and Cheng, P. W.: On the design of measurement campaigns for fatigue life monitoring of offshore wind turbines, Proceedings of the twenty-third International Offshore and Polar Engineering Conference, June 2013, Alaska, Paper No. I-13-041, ISBN 978-1 880653 99-9, 2013. a
Topham, E. and McMillan, D.:
Sustainable decommissioning of an offshore wind farm, Renew. Energ., 102, 470–480, 2017. a
Weijtjens, W., Noppe, N., Verbelen, T., Iliopoulos, A., and Devriendt, C.: Offshore wind turbine foundation monitoring, extrapolating fatigue measurements from fleet leaders to the entire wind farm, J. Phys.-Conf. Ser., 753, 092018, https://doi.org/10.1088/1742-6596/753/9/092018, 2016.
a, b
Ziegler, L. and Muskulus, M.: Fatigue reassessment for lifetime extension of offshore wind monopile substructures, J. Phys.-Conf. Ser., 753, 092010, https://doi.org/10.1088/1742-6596/753/9/092010, 2016a. a
Ziegler, L. and Muskulus, M.: Lifetime extension of offshore wind monopiles: Assessment process and relevance of fatigue crack inspection, Proceedings of the 12th EA WE PhD Seminar on Wind Energy in Europe, 25–27 May 2016, Lyngby, Denmark, http://awesome-h2020.eu/wp-content/uploads/2017/10/Download_1.pdf (last access: 19 September 2022), 2016b. a
Ziegler, L., Smolka, U., Cosack, N., and Muskulus, M.: Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?, Wind Energ. Sci., 2, 469–476, https://doi.org/10.5194/wes-2-469-2017, 2017. a, b
Short summary
Offshore wind turbines are beginning to reach their design lifetimes. Hence, lifetime extensions are becoming relevant. To make well-founded decisions on possible lifetime extensions, fatigue damage predictions are required. Measurement-based assessments instead of simulation-based analyses have rarely been conducted so far, since data are limited. Therefore, this work focuses on the temporal extrapolation of measurement data. It is shown that fatigue damage can be extrapolated accurately.
Offshore wind turbines are beginning to reach their design lifetimes. Hence, lifetime extensions...
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