IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v221y2024ics0960148123016981.html
   My bibliography  Save this article

Research on rapid calculation method of wind turbine blade strain for digital twin

Author

Listed:
  • Wang, Bingkai
  • Sun, Wenlei
  • Wang, Hongwei
  • Xu, Tiantian
  • Zou, Yi

Abstract

Digital twin (DT) technology provides unlimited possibilities for remote intelligent operation and maintenance of wind turbine blade (WTB) in operation. However, the urgent demand for high accuracy and real-time performance limit the engineering applications of this technology. This article presents a novel rapid calculation method of WTB strain, which aims to break the technical difficulties of real-time calculation in digital twin and realize synchronous mapping of WTB health status. Firstly, the load acting on the WTB is simplified according to the equivalent conversion theory. Then, a simplified finite element model, namely mechanism calculation model, of WTB is built based on mechanics. The multi-fidelity surrogate model is innovatively adopted to calibrate the results of the mechanism calculation via a small amount of data measured via sensors on the WTB. Based on the calibrated results, a unique strain field estimation model is built to provide more comprehensive information about WTB. Finally, the proposed method is verified via an experiment about WTB strain measurement and numerical simulation. The results demonstrate a considerable advancement in real-time performance with approximately 1444× compared to the traditional finite element method. The shortest elapsed time is 0.91s. Moreover, the minimum relative error is 0.11 % and the average relative error is less than 0.76 %.

Suggested Citation

  • Wang, Bingkai & Sun, Wenlei & Wang, Hongwei & Xu, Tiantian & Zou, Yi, 2024. "Research on rapid calculation method of wind turbine blade strain for digital twin," Renewable Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:renene:v:221:y:2024:i:c:s0960148123016981
    DOI: 10.1016/j.renene.2023.119783
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123016981
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119783?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wymore, Mathew L. & Van Dam, Jeremy E. & Ceylan, Halil & Qiao, Daji, 2015. "A survey of health monitoring systems for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 976-990.
    2. de Oliveira, M. & Puraca, R.C. & Carmo, B.S., 2022. "Blade-resolved numerical simulations of the NREL offshore 5 MW baseline wind turbine in full scale: A study of proper solver configuration and discretization strategies," Energy, Elsevier, vol. 254(PB).
    3. Dai, Juchuan & Li, Mimi & Chen, Huanguo & He, Tao & Zhang, Fan, 2022. "Progress and challenges on blade load research of large-scale wind turbines," Renewable Energy, Elsevier, vol. 196(C), pages 482-496.
    4. Cheng, Xu & Shi, Fan & Liu, Yongping & Liu, Xiufeng & Huang, Lizhen, 2022. "Wind turbine blade icing detection: a federated learning approach," Energy, Elsevier, vol. 254(PC).
    5. Johansen, Katinka, 2021. "Blowing in the wind: A brief history of wind energy and wind power technologies in Denmark," Energy Policy, Elsevier, vol. 152(C).
    6. Moynihan, Bridget & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2022. "Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification," Renewable Energy, Elsevier, vol. 184(C), pages 662-676.
    7. Alkhabbaz, Ali & Yang, Ho-Seong & Weerakoon, A.H Samitha & Lee, Young-Ho, 2021. "A novel linearization approach of chord and twist angle distribution for 10 kW horizontal axis wind turbine," Renewable Energy, Elsevier, vol. 178(C), pages 1398-1420.
    8. Verma, Amrit Shankar & Yan, Jiquan & Hu, Weifei & Jiang, Zhiyu & Shi, Wei & Teuwen, Julie J.E., 2023. "A review of impact loads on composite wind turbine blades: Impact threats and classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    9. Wang, Lin & Liu, Xiongwei & Renevier, Nathalie & Stables, Matthew & Hall, George M., 2014. "Nonlinear aeroelastic modelling for wind turbine blades based on blade element momentum theory and geometrically exact beam theory," Energy, Elsevier, vol. 76(C), pages 487-501.
    10. Zhao, Xiang & Dao, My Ha & Le, Quang Tuyen, 2023. "Digital twining of an offshore wind turbine on a monopile using reduced-order modelling approach," Renewable Energy, Elsevier, vol. 206(C), pages 531-551.
    11. Chen, Bin & Yu, Songhao & Yu, Yang & Zhou, Yilin, 2020. "Acoustical damage detection of wind turbine blade using the improved incremental support vector data description," Renewable Energy, Elsevier, vol. 156(C), pages 548-557.
    12. Miao, Haozeyu & Xu, Haiming & Huang, Gang & Yang, Kai, 2023. "Evaluation and future projections of wind energy resources over the Northern Hemisphere in CMIP5 and CMIP6 models," Renewable Energy, Elsevier, vol. 211(C), pages 809-821.
    Full references (including those not matched with items on IDEAS)

    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. Liang, Jun & Fu, Yuhao & Wang, Ying & Ou, Jinping, 2024. "Identification of equivalent wind and wave loads for monopile-supported offshore wind turbines in operating condition," Renewable Energy, Elsevier, vol. 237(PA).
    2. Li, Jichao & Dao, My Ha & Le, Quang Tuyen, 2024. "Data-driven modal parameterization for robust aerodynamic shape optimization of wind turbine blades," Renewable Energy, Elsevier, vol. 224(C).
    3. Peng, Weike & Gao, Jiaxin & Chen, Yuntian & Wang, Shengwei, 2024. "Bridging data barriers among participants: Assessing the potential of geoenergy through federated learning," Applied Energy, Elsevier, vol. 367(C).
    4. Miguel Moreira & Frederico Rodrigues & Sílvio Cândido & Guilherme Santos & José Páscoa, 2023. "Development of a Background-Oriented Schlieren (BOS) System for Thermal Characterization of Flow Induced by Plasma Actuators," Energies, MDPI, vol. 16(1), pages 1-17, January.
    5. Chen, Bei & Hua, Xugang & Zhang, Zili & Nielsen, Søren R.K. & Chen, Zhengqing, 2021. "Active flutter control of the wind turbines using double-pitched blades," Renewable Energy, Elsevier, vol. 163(C), pages 2081-2097.
    6. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    7. Li, Shoutu & Chen, Qin & Li, Ye & Pröbsting, Stefan & Yang, Congxin & Zheng, Xiaobo & Yang, Yannian & Zhu, Weijun & Shen, Wenzhong & Wu, Faming & Li, Deshun & Wang, Tongguang & Ke, Shitang, 2022. "Experimental investigation on noise characteristics of small scale vertical axis wind turbines in urban environments," Renewable Energy, Elsevier, vol. 200(C), pages 970-982.
    8. Rubert, T. & Zorzi, G. & Fusiek, G. & Niewczas, P. & McMillan, D. & McAlorum, J. & Perry, M., 2019. "Wind turbine lifetime extension decision-making based on structural health monitoring," Renewable Energy, Elsevier, vol. 143(C), pages 611-621.
    9. Lapa, Gabriel Vicentin Pereira & Gay Neto, Alfredo & Franzini, Guilherme Rosa, 2023. "Effects of blade torsion on IEA 15MW turbine rotor operation," Renewable Energy, Elsevier, vol. 219(P2).
    10. Saulius Baskutis & Jolanta Baskutiene & Valentinas Navickas & Yuriy Bilan & Wojciech Cieśliński, 2021. "Perspectives and Problems of Using Renewable Energy Sources and Implementation of Local “Green” Initiatives: A Regional Assessment," Energies, MDPI, vol. 14(18), pages 1-16, September.
    11. Igliński, Bartłomiej & Iglińska, Anna & Koziński, Grzegorz & Skrzatek, Mateusz & Buczkowski, Roman, 2016. "Wind energy in Poland – History, current state, surveys, Renewable Energy Sources Act, SWOT analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 19-33.
    12. Johansen, K. & Upham, P.J., 2025. "Should end-users take their clothes off inside on a cold winter's day? Sustainability pressures on district heating professionals in Denmark," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    13. Zhang, Wenguang & Bai, Xuejian & Wang, Yifeng & Han, Yue & Hu, Yong, 2018. "Optimization of sizing parameters and multi-objective control of trailing edge flaps on a smart rotor," Renewable Energy, Elsevier, vol. 129(PA), pages 75-91.
    14. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    15. Johansen, Katinka & Johra, Hicham, 2022. "A niche technique overlooked in the Danish district heating sector? Exploring socio-technical perspectives of short-term thermal energy storage for building energy flexibility," Energy, Elsevier, vol. 256(C).
    16. Burgaç, Alper & Yavuz, Hakan, 2019. "Fuzzy Logic based hybrid type control implementation of a heaving wave energy converter," Energy, Elsevier, vol. 170(C), pages 1202-1214.
    17. Shine Win Naung & Mohammad Rahmati & Htet Shine, 2025. "High-Fidelity Aeroelastic Analysis of a Wind Turbine Using a Nonlinear Frequency-Domain Solution Method," Energies, MDPI, vol. 18(5), pages 1-20, February.
    18. Wei-Hua Hu & De-Hui Tang & Ming Wang & Jun-Le Liu & Zuo-Hua Li & Wei Lu & Jun Teng & Samir Said & Rolf. G. Rohrmann, 2020. "Resonance Monitoring of a Horizontal Wind Turbine by Strain-Based Automated Operational Modal Analysis," Energies, MDPI, vol. 13(3), pages 1-21, January.
    19. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    20. Haojie Kang & Bofeng Xu & Xiang Shen & Zhen Li & Xin Cai & Zhiqiang Hu, 2023. "Comparison of Blade Aeroelastic Responses between Upwind and Downwind of 10 MW Wind Turbines under the Shear Wind Condition," Energies, MDPI, vol. 16(6), pages 1-13, March.

    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:eee:renene:v:221:y:2024:i:c:s0960148123016981. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.