IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v221y2024ics0960148123017810.html
   My bibliography  Save this article

Field load testing of wind turbines based on the relational model of strain vs load

Author

Listed:
  • Dai, Juchuan
  • Li, Mimi
  • Zhang, Fan
  • Zeng, Huifan

Abstract

Compared to theoretical calculations, field blade load testing can more accurately obtain actual load characteristics. The main challenge is the field relationship correction of strain vs load. The initial state of the strain sensing installation can be divided into two scenarios. One scenario is to install before the blade is lifted and another is to install online during the service of wind turbines. The key analysis is conducted on the field correction of the relationship of strain vs load, and corresponding correction strategies are constructed. A decoupled calculation model for strains in the edgewise direction induced by the aerodynamic moment and the gravity moment is proposed. Ground and online experiments for blade strain testing have been carried out. Through the combination of strain testing data and SCADA data, some new insights have been gained on strain characteristics. For example, the mapping coefficient of strain vs load induced by the aerodynamic moment varies under different operating conditions, while the mapping coefficient of strain vs load induced by gravitational moment is relatively stable. When the power of the wind turbine is 552 kW, 853 kW, and 1379 kW, the coefficients induced by the aerodynamic moment are 2270 N m/μɛ, 2480 N m/μɛ, and 3224 N m/μɛ, respectively.

Suggested Citation

  • Dai, Juchuan & Li, Mimi & Zhang, Fan & Zeng, Huifan, 2024. "Field load testing of wind turbines based on the relational model of strain vs load," Renewable Energy, Elsevier, vol. 221(C).
  • Handle: RePEc:eee:renene:v:221:y:2024:i:c:s0960148123017810
    DOI: 10.1016/j.renene.2023.119866
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123017810
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2023.119866?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. 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).
    2. Dai, J.C. & Hu, Y.P. & Liu, D.S. & Long, X., 2011. "Aerodynamic loads calculation and analysis for large scale wind turbine based on combining BEM modified theory with dynamic stall model," Renewable Energy, Elsevier, vol. 36(3), pages 1095-1104.
    3. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    4. Namahoro, J.P. & Wu, Q. & Su, H., 2023. "Wind energy, industrial-economic development and CO2 emissions nexus: Do droughts matter?," Energy, Elsevier, vol. 278(PA).
    5. Summerfield-Ryan, Oliver & Park, Susan, 2023. "The power of wind: The global wind energy industry's successes and failures," Ecological Economics, Elsevier, vol. 210(C).
    6. Huifan Zeng & Juchuan Dai & Chengming Zuo & Huanguo Chen & Mimi Li & Fan Zhang, 2022. "Correlation Investigation of Wind Turbine Multiple Operating Parameters Based on SCADA Data," Energies, MDPI, vol. 15(14), pages 1-24, July.
    7. Novaes Menezes, Eduardo José & Araújo, Alex Maurício & Rohatgi, Janardan Singh & González del Foyo, Pedro Manuel, 2018. "Active load control of large wind turbines using state-space methods and disturbance accommodating control," Energy, Elsevier, vol. 150(C), pages 310-319.
    8. Pavese, Christian & Kim, Taeseong & Murcia, Juan Pablo, 2017. "Design of a wind turbine swept blade through extensive load analysis," Renewable Energy, Elsevier, vol. 102(PA), pages 21-34.
    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. Fan Zhang & Juchuan Dai & Deshun Liu & Linxing Li & Xin Long, 2019. "Investigation of the Pitch Load of Large-Scale Wind Turbines Using Field SCADA Data," Energies, MDPI, vol. 12(3), pages 1-20, February.
    2. Jingchun Chu & Ling Yuan & Yang Hu & Chenyang Pan & Lei Pan, 2019. "Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine," Energies, MDPI, vol. 12(18), pages 1-24, September.
    3. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    4. Nejra Beganovic & Jackson G. Njiri & Dirk Söffker, 2018. "Reduction of Structural Loads in Wind Turbines Based on an Adapted Control Strategy Concerning Online Fatigue Damage Evaluation Models," Energies, MDPI, vol. 11(12), pages 1-15, December.
    5. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    6. 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).
    7. Mian Du & Jun Yi & Peyman Mazidi & Lin Cheng & Jianbo Guo, 2017. "A Parameter Selection Method for Wind Turbine Health Management through SCADA Data," Energies, MDPI, vol. 10(2), pages 1-14, February.
    8. Sessarego, Matias & Feng, Ju & Ramos-García, Néstor & Horcas, Sergio González, 2020. "Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow," Renewable Energy, Elsevier, vol. 146(C), pages 1524-1535.
    9. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    10. Miriam L. A. Gemaque & Jerson R. P. Vaz & Osvaldo R. Saavedra, 2022. "Optimization of Hydrokinetic Swept Blades," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    11. Gambuzza, Stefano & Pisetta, Gabriele & Davey, Thomas & Steynor, Jeffrey & Viola, Ignazio Maria, 2023. "Model-scale experiments of passive pitch control for tidal turbines," Renewable Energy, Elsevier, vol. 205(C), pages 10-29.
    12. Ravi Pandit & David Infield, 2018. "Gaussian Process Operational Curves for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 11(7), pages 1-20, June.
    13. 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.
    14. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    15. Gilberto Santo & Mathijs Peeters & Wim Van Paepegem & Joris Degroote, 2019. "Numerical Investigation of the Effect of Tower Dam and Rotor Misalignment on Performance and Loads of a Large Wind Turbine in the Atmospheric Boundary Layer," Energies, MDPI, vol. 12(7), pages 1-19, March.
    16. Dai, Juchuan & Yang, Xin & Hu, Wei & Wen, Li & Tan, Yayi, 2018. "Effect investigation of yaw on wind turbine performance based on SCADA data," Energy, Elsevier, vol. 149(C), pages 684-696.
    17. Martinez, A. & Iglesias, G., 2024. "Global wind energy resources decline under climate change," Energy, Elsevier, vol. 288(C).
    18. Pan He & Jian Xia, 2022. "Study on the Influence of Low-Level Jet on the Aerodynamic Characteristics of Horizontal Axis Wind Turbine Rotor Based on the Aerodynamics–Controller Interaction Method," Energies, MDPI, vol. 15(8), pages 1-18, April.
    19. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.
    20. Chi-Jeng Bai & Wei-Cheng Wang & Po-Wei Chen & Wen-Tong Chong, 2014. "System Integration of the Horizontal-Axis Wind Turbine: The Design of Turbine Blades with an Axial-Flux Permanent Magnet Generator," Energies, MDPI, vol. 7(11), pages 1-21, November.

    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:s0960148123017810. 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.