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Elastic actuator line modelling for wake-induced fatigue analysis of horizontal axis wind turbine blade

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  • Meng, Hang
  • Lien, Fue-Sang
  • Li, Li

Abstract

Wake effect causes fatigue increase on the horizontal axis wind turbine (HAWT) blades. This wake-induced fatigue has significant impacts on the efficiency and lifespan of the whole wind farm. However, conventional aeroelastic codes are deficient in terms of turbulent wake modelling and wake interaction modelling. To accurately carry out the aeroelastic simulation in multi-wake operation, an “elastic actuator line” (EAL) model is proposed. Essentially, this model is the combination of the actuator line (AL) wake model and a finite difference structural model. The present research includes two parts. Firstly, the proposed EAL model is outlined. To better establish the two-way coupling between the structural model and the AL model, the transformation of a set of structural equations is presented. Secondly, numerical structural model is established. To verify the present model, the simulated results by EAL for a single NREL 5 MW turbine are compared with those obtained with the aeroelastic code FAST. And the comparison shows a good agreement for both high and low TSRs (Tip-Speed-Ratios). Another case study for the wake interaction involving two staggered HAWTs is also carried out, which shows that the downstream wind turbine truly experiences an obvious wake-induced fatigue increase based on our equivalent fatigue load analysis.

Suggested Citation

  • Meng, Hang & Lien, Fue-Sang & Li, Li, 2018. "Elastic actuator line modelling for wake-induced fatigue analysis of horizontal axis wind turbine blade," Renewable Energy, Elsevier, vol. 116(PA), pages 423-437.
    • Handle: RePEc:eee:renene:v:116:y:2018:i:pa:p:423-437
    DOI: 10.1016/j.renene.2017.08.074
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    Cited by:

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    3. Fei Zhao & Yihan Gao & Tengyuan Wang & Jinsha Yuan & Xiaoxia Gao, 2020. "Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
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    6. Gao, Xiaoxia & Chen, Yao & Xu, Shinai & Gao, Wei & Zhu, Xiaoxun & Sun, Haiying & Yang, Hongxing & Han, Zhonghe & Wang, Yu & Lu, Hao, 2022. "Comparative experimental investigation into wake characteristics of turbines in three wind farms areas with varying terrain complexity from LiDAR measurements," Applied Energy, Elsevier, vol. 307(C).
    7. Fei, Zhao & Tengyuan, Wang & Xiaoxia, Gao & Haiying, Sun & Hongxing, Yang & Zhonghe, Han & Yu, Wang & Xiaoxun, Zhu, 2020. "Experimental study on wake interactions and performance of the turbines with different rotor-diameters in adjacent area of large-scale wind farm," Energy, Elsevier, vol. 199(C).
    8. Wei Li & Shinai Xu & Baiyun Qian & Xiaoxia Gao & Xiaoxun Zhu & Zeqi Shi & Wei Liu & Qiaoliang Hu, 2022. "Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
    9. Mou Lin & Fernando Porté-Agel, 2023. "Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control," Energies, MDPI, vol. 16(6), pages 1-17, March.
    10. Lu, Liang & Wu, Haijun & Wu, Jianzhong, 2021. "A case study for the optimization of moment-matching in wind turbine blade fatigue tests with a resonant type exciting approach," Renewable Energy, Elsevier, vol. 174(C), pages 769-785.
    11. Meng, Hang & Li, Li & Zhang, Jinhua, 2020. "A preliminary numerical study of the wake effects on the fatigue load for wind farm based on elastic actuator line model," Renewable Energy, Elsevier, vol. 162(C), pages 788-801.
    12. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    13. Wang, Tengyuan & Cai, Chang & Wang, Xinbao & Wang, Zekun & Chen, Yewen & Song, Juanjuan & Xu, Jianzhong & Zhang, Yuning & Li, Qingan, 2023. "A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow," Energy, Elsevier, vol. 271(C).
    14. Zhe Ma & Liping Lei & Earl Dowell & Pan Zeng, 2020. "An Experimental Study on the Actuator Line Method with Anisotropic Regularization Kernel," Energies, MDPI, vol. 13(4), pages 1-19, February.
    15. Christian Santoni & Fotis Sotiropoulos & Ali Khosronejad, 2024. "A Comparative Analysis of Actuator-Based Turbine Structure Parametrizations for High-Fidelity Modeling of Utility-Scale Wind Turbines under Neutral Atmospheric Conditions," Energies, MDPI, vol. 17(3), pages 1-16, February.

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