IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i6p1306-d331363.html
   My bibliography  Save this article

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

Author

Listed:
  • Christos Galinos

    (Technical University of Denmark, Department of Wind Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark)

  • Jonas Kazda

    (Technical University of Denmark, Department of Wind Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark)

  • Wai Hou Lio

    (Technical University of Denmark, Department of Wind Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark)

  • Gregor Giebel

    (Technical University of Denmark, Department of Wind Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark)

Abstract

Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg–Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model.

Suggested Citation

  • Christos Galinos & Jonas Kazda & Wai Hou Lio & Gregor Giebel, 2020. "T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case," Energies, MDPI, vol. 13(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1306-:d:331363
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/6/1306/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/6/1306/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ren, Guorui & Liu, Jinfu & Wan, Jie & Li, Fei & Guo, Yufeng & Yu, Daren, 2018. "The analysis of turbulence intensity based on wind speed data in onshore wind farms," Renewable Energy, Elsevier, vol. 123(C), pages 756-766.
    2. Göçmen, Tuhfe & Giebel, Gregor, 2016. "Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms," Renewable Energy, Elsevier, vol. 99(C), pages 524-532.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Georgios Gasparis & Wai Hou Lio & Fanzhong Meng, 2020. "Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm," Energies, MDPI, vol. 13(23), pages 1-15, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Öztürk, Buğrahan & Hassanein, Abdelrahman & Akpolat, M Tuğrul & Abdulrahim, Anas & Perçin, Mustafa & Uzol, Oğuz, 2023. "On the wake characteristics of a model wind turbine and a porous disc: Effects of freestream turbulence intensity," Renewable Energy, Elsevier, vol. 212(C), pages 238-250.
    2. Asadi, Meysam & Pourhossein, Kazem, 2021. "Wind farm site selection considering turbulence intensity," Energy, Elsevier, vol. 236(C).
    3. Sergio Campobasso, M. & Castorrini, Alessio & Ortolani, Andrea & Minisci, Edmondo, 2023. "Probabilistic analysis of wind turbine performance degradation due to blade erosion accounting for uncertainty of damage geometry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    4. Gonçalves, Afonso N.C. & Pereira, José M.C. & Sousa, João M.M., 2022. "Passive control of dynamic stall in a H-Darrieus Vertical Axis Wind Turbine using blade leading-edge protuberances," Applied Energy, Elsevier, vol. 324(C).
    5. Li, Li & Wang, Bing & Ge, Mingwei & Huang, Zhi & Li, Xintao & Liu, Yongqian, 2023. "A novel superposition method for streamwise turbulence intensity of wind-turbine wakes," Energy, Elsevier, vol. 276(C).
    6. C. A. Lopez-Villalobos & O. Rodriguez-Hernandez & R. Campos-Amezcua & Guillermo Hernandez-Cruz & O. A. Jaramillo & J. L. Mendoza, 2018. "Wind Turbulence Intensity at La Ventosa, Mexico: A Comparative Study with the IEC61400 Standards," Energies, MDPI, vol. 11(11), pages 1-19, November.
    7. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    8. Besseau, Romain & Sacchi, Romain & Blanc, Isabelle & Pérez-López, Paula, 2019. "Past, present and future environmental footprint of the Danish wind turbine fleet with LCA_WIND_DK, an online interactive platform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 274-288.
    9. Liao, Ding & Zhu, Shun-Peng & Correia, José A.F.O. & De Jesus, Abílio M.P. & Veljkovic, Milan & Berto, Filippo, 2022. "Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects," Renewable Energy, Elsevier, vol. 200(C), pages 724-742.
    10. Dong, Liang & Lio, Wai Hou & Pirrung, Georg Raimund, 2021. "Analysis and design of an adaptive turbulence-based controller for wind turbines," Renewable Energy, Elsevier, vol. 178(C), pages 730-744.
    11. Christy Pérez & Michel Rivero & Mauricio Escalante & Victor Ramirez & Damien Guilbert, 2023. "Influence of Atmospheric Stability on Wind Turbine Energy Production: A Case Study of the Coastal Region of Yucatan," Energies, MDPI, vol. 16(10), pages 1-20, May.
    12. Yuewei Liu & Shenghui Zhang & Xuejun Chen & Jianzhou Wang, 2018. "Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting," Sustainability, MDPI, vol. 10(12), pages 1-30, December.
    13. Yazhou Wang & Xin Cai & Shifa Lin & Bofeng Xu & Yuan Zhang & Saixian Bian, 2022. "Study of Tower Clearance Safety Protection during Extreme Gust Based on Wind Turbine Monitoring Data," Energies, MDPI, vol. 15(12), pages 1-11, June.
    14. Li, Wenzhe & Jia, Xiaodong & Li, Xiang & Wang, Yinglu & Lee, Jay, 2021. "A Markov model for short term wind speed prediction by integrating the wind acceleration information," Renewable Energy, Elsevier, vol. 164(C), pages 242-253.
    15. Nezhad, M. Majidi & Neshat, M. & Heydari, A. & Razmjoo, A. & Piras, G. & Garcia, D. Astiaso, 2021. "A new methodology for offshore wind speed assessment integrating Sentinel-1, ERA-Interim and in-situ measurement," Renewable Energy, Elsevier, vol. 172(C), pages 1301-1313.
    16. Bilal, Boudy & Adjallah, Kondo Hloindo & Yetilmezsoy, Kaan & Bahramian, Majid & Kıyan, Emel, 2021. "Determination of wind potential characteristics and techno-economic feasibility analysis of wind turbines for Northwest Africa," Energy, Elsevier, vol. 218(C).
    17. Arenas-López, J. Pablo & Badaoui, Mohamed, 2020. "Stochastic modelling of wind speeds based on turbulence intensity," Renewable Energy, Elsevier, vol. 155(C), pages 10-22.
    18. Yang, Jian & Wang, Li & Song, Dongran & Huang, Chaoneng & Huang, Liansheng & Wang, Junlei, 2022. "Incorporating environmental impacts into zero-point shifting diagnosis of wind turbines yaw angle," Energy, Elsevier, vol. 238(PA).
    19. Tiainen, Jonna & Grönman, Aki & Jaatinen-Värri, Ahti & Pyy, Lauri, 2020. "Effect of non-ideally manufactured riblets on airfoil and wind turbine performance," Renewable Energy, Elsevier, vol. 155(C), pages 79-89.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1306-:d:331363. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.