IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p102-d1010953.html
   My bibliography  Save this article

Optimisation of Highly Efficient Composite Blades for Retrofitting Existing Wind Turbines

Author

Listed:
  • Yadong Jiang

    (SFI MaREI Centre for Energy, Climate and Marine, Ryan Institute & School of Engineering, University of Galway, H91 TK33 Galway, Ireland)

  • William Finnegan

    (SFI MaREI Centre for Energy, Climate and Marine, Ryan Institute & School of Engineering, University of Galway, H91 TK33 Galway, Ireland)

  • Tomas Flanagan

    (ÉireComposites Teo, H91 Y923 Galway, Ireland)

  • Jamie Goggins

    (SFI MaREI Centre for Energy, Climate and Marine, Ryan Institute & School of Engineering, University of Galway, H91 TK33 Galway, Ireland)

Abstract

Currently, wind energy, a reliable, affordable, and clean energy source, contributes to 16% of Europe’s electricity. A typical modern wind turbine design lifespan is 20 years. In European Union countries, the number of wind turbines reaching 20 years or older will become significant beyond 2025. This research study presents a methodology aiming to upgrade rotor blades for existing wind turbines to extend the turbine life. This methodology employs blade element momentum theory, finite element analysis, genetic algorithm, and direct screen methods to optimise the blade external geometry and structural design, with the main objective to increase the blade power capture efficiency and enhance its structural performance. Meanwhile, the compatibility between the blade and the existing rotor of the wind turbine is considered during the optimisation. By applying this methodology to a 225 kW wind turbine, an optimal blade, which is compatible with the turbine hub, is proposed with the assistance of physical testing data. The optimised blade, which benefits from high-performance carbon-fibre composite material and layup optimisation, has a reduced tip deflection and self-weight of 48% and 31%, respectively, resulting in a significant reduction in resources, while improving its structural performance. In addition, for the optimised blade, there is an improvement in the power production of approximately 10.5% at a wind speed of 11 m/s, which results in an increase of over 4.2% in average annual power production compared to the existing turbine, without changing the blade length. Furthermore, an advanced aero-elastic-based simulation is conducted to ensure the changes made to the blade can guarantee an operation life of at least 20 years, which is equivalent to that of the reference blade.

Suggested Citation

  • Yadong Jiang & William Finnegan & Tomas Flanagan & Jamie Goggins, 2022. "Optimisation of Highly Efficient Composite Blades for Retrofitting Existing Wind Turbines," Energies, MDPI, vol. 16(1), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:102-:d:1010953
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/102/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/102/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Qiang & Luo, Kun & Yuan, Renyu & Wang, Shuai & Fan, Jianren & Cen, Kefa, 2020. "A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain," Energy, Elsevier, vol. 203(C).
    2. Shen, Xin & Chen, Jin-Ge & Zhu, Xiao-Cheng & Liu, Peng-Yin & Du, Zhao-Hui, 2015. "Multi-objective optimization of wind turbine blades using lifting surface method," Energy, Elsevier, vol. 90(P1), pages 1111-1121.
    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. He, Ruiyang & Yang, Hongxing & Lu, Lin & Gao, Xiaoxia, 2024. "Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms," Renewable Energy, Elsevier, vol. 225(C).
    2. Baniassadi, Amir & Shirinbakhsh, Mehrdad & Torabi, Farschad, 2017. "Multivariate optimization of off-grid wind turbines with variable demand - Case study of a remote commercial building," Renewable Energy, Elsevier, vol. 101(C), pages 1021-1029.
    3. Zhenye Sun & Wei Jun Zhu & Wen Zhong Shen & Wei Zhong & Jiufa Cao & Qiuhan Tao, 2020. "Aerodynamic Analysis of Coning Effects on the DTU 10 MW Wind Turbine Rotor," Energies, MDPI, vol. 13(21), pages 1-19, November.
    4. Vianna Neto, Júlio Xavier & Guerra Junior, Elci José & Moreno, Sinvaldo Rodrigues & Hultmann Ayala, Helon Vicente & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2018. "Wind turbine blade geometry design based on multi-objective optimization using metaheuristics," Energy, Elsevier, vol. 162(C), pages 645-658.
    5. Wang, Qiang & Luo, Kun & Wu, Chunlei & Zhu, Zhaofan & Fan, Jianren, 2022. "Mesoscale simulations of a real onshore wind power base in complex terrain: Wind farm wake behavior and power production," Energy, Elsevier, vol. 241(C).
    6. Sun, Haiying & Yang, Hongxing & Gao, Xiaoxia, 2023. "Investigation into wind turbine wake effect on complex terrain," Energy, Elsevier, vol. 269(C).
    7. Mattia Silei & Stefania Bellavia & Francesco Superchi & Alessandro Bianchini, 2023. "Recovering Corrupted Data in Wind Farm Measurements: A Matrix Completion Approach," Energies, MDPI, vol. 16(4), pages 1-32, February.
    8. Longfu Luo & Xiaofeng Zhang & Dongran Song & Weiyi Tang & Jian Yang & Li Li & Xiaoyu Tian & Wu Wen, 2018. "Optimal Design of Rated Wind Speed and Rotor Radius to Minimizing the Cost of Energy for Offshore Wind Turbines," Energies, MDPI, vol. 11(10), pages 1-17, October.
    9. Zhang, Ye & Deng, Shuanghou & Wang, Xiaofang, 2019. "RANS and DDES simulations of a horizontal-axis wind turbine under stalled flow condition using OpenFOAM," Energy, Elsevier, vol. 167(C), pages 1155-1163.
    10. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
    11. Wang, Xianxun & Mei, Yadong & Kong, Yanjun & Lin, Yuru & Wang, Hao, 2017. "Improved multi-objective model and analysis of the coordinated operation of a hydro-wind-photovoltaic system," Energy, Elsevier, vol. 134(C), pages 813-839.
    12. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2017. "Continuous-time tube-based explicit model predictive control for collective pitching of wind turbines," Energy, Elsevier, vol. 118(C), pages 1222-1233.
    13. De Moliner, Giorgia & Giani, Paolo & Lonati, Giovanni & Crippa, Paola, 2024. "Sensitivity of multiscale large Eddy simulations for wind power calculations in complex terrain," Applied Energy, Elsevier, vol. 364(C).
    14. Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems & M. Mujahid Rafique, 2018. "Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review," Energies, MDPI, vol. 11(3), pages 1-34, February.
    15. Sun, ZhaoCheng & Li, Dong & Mao, YuFeng & Feng, Long & Zhang, Yue & Liu, Chao, 2022. "Anti-cavitation optimal design and experimental research on tidal turbines based on improved inverse BEM," Energy, Elsevier, vol. 239(PD).
    16. Song, Dongran & Yang, Yinggang & Zheng, Songyue & Tang, Weiyi & Yang, Jian & Su, Mei & Yang, Xuebing & Joo, Young Hoon, 2019. "Capacity factor estimation of variable-speed wind turbines considering the coupled influence of the QN-curve and the air density," Energy, Elsevier, vol. 183(C), pages 1049-1060.
    17. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    18. Pedruzzi, Rizzieri & Silva, Allan Rodrigues & Soares dos Santos, Thalyta & Araujo, Allan Cavalcante & Cotta Weyll, Arthur Lúcide & Lago Kitagawa, Yasmin Kaore & Nunes da Silva Ramos, Diogo & Milani de, 2023. "Review of mapping analysis and complementarity between solar and wind energy sources," Energy, Elsevier, vol. 283(C).
    19. Wang, Qi & Suo, Ruixia & Han, Qiutong, 2024. "A study on natural gas consumption forecasting in China using the LMDI-PSO-LSTM model: Factor decomposition and scenario analysis," Energy, Elsevier, vol. 292(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:16:y:2022:i:1:p:102-:d:1010953. 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.