IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i10p2279-d1391073.html
   My bibliography  Save this article

Sand-Laden Wind Erosion Pair Experimental Analysis of Aerodynamic Performance of the Wind Turbine Blades

Author

Listed:
  • Daqian Wan

    (School of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Songli Chen

    (School of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Danlan Li

    (School of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Qi Zhen

    (School of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Bo Zhang

    (School of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

Abstract

In the Inner Mongolia region, sand and dust storms are prevalent throughout the year, with sand erosion having a particularly significant impact on the performance of wind turbine blades. To enhance the performance stability of wind turbines and reduce operation and maintenance costs, this study delves into the specific impact of sand-laden wind erosion on the aerodynamic performance of scaled-down wooden wind turbine blades. The experiment conducts vehicle-mounted tests on scaled models of 1.5 MW wind turbine blades that have been eroded by wind-sand flows from different zones, analyzing the changes in aerodynamic performance of wind turbines caused by the erosion. The results indicate that with an increase in the angle of installation, both the overall power output and the wind energy utilization coefficient of the wind turbines show a declining trend. The power outputs of both the partially eroded group and the fully eroded group are unable to reach the rated power level of 100 W. Compared to the uneroded group, the leading-edge eroded group demonstrated higher power output and wind energy utilization coefficients across most wind speed ranges. This finding verifies the possibility that the drag-reducing effect caused by pits from leading-edge erosion has a positive impact on the aerodynamic performance of the blades. It also provides a new research perspective and strong evidence for the study of erosion effects on wind turbine blades and the optimization of their aerodynamic performance.

Suggested Citation

  • Daqian Wan & Songli Chen & Danlan Li & Qi Zhen & Bo Zhang, 2024. "Sand-Laden Wind Erosion Pair Experimental Analysis of Aerodynamic Performance of the Wind Turbine Blades," Energies, MDPI, vol. 17(10), pages 1-17, May.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2279-:d:1391073
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/10/2279/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/10/2279/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function," Renewable Energy, Elsevier, vol. 181(C), pages 59-70.
    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. 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).
    2. Tang, Yaochi & Chang, Yunchi & Li, Kuohao, 2023. "Applications of K-nearest neighbor algorithm in intelligent diagnosis of wind turbine blades damage," Renewable Energy, Elsevier, vol. 212(C), pages 855-864.
    3. Wanyi Deng & Xiaoxue Ma & Weiliang Qiao, 2024. "A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy," Mathematics, MDPI, vol. 12(16), pages 1-17, August.
    4. Xiaoxun, Zhu & Xinyu, Hang & Xiaoxia, Gao & Xing, Yang & Zixu, Xu & Yu, Wang & Huaxin, Liu, 2022. "Research on crack detection method of wind turbine blade based on a deep learning method," Applied Energy, Elsevier, vol. 328(C).
    5. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.
    6. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
    7. Wenjie Wang & Yu Xue & Chengkuan He & Yongnian Zhao, 2022. "Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades," Energies, MDPI, vol. 15(15), pages 1-31, August.
    8. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
    9. Kaewniam, Panida & Cao, Maosen & Alkayem, Nizar Faisal & Li, Dayang & Manoach, Emil, 2022. "Recent advances in damage detection of wind turbine blades: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    10. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy," Applied Energy, Elsevier, vol. 313(C).

    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:17:y:2024:i:10:p:2279-:d:1391073. 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.