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Proposal of a novel GPU-accelerated lifetime optimization method for onshore wind turbine dampers under real wind distribution

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  • Liu, Zhenqing
  • Wang, Yize
  • Nyangi, Patrice
  • Zhu, Zhiwen
  • Hua, Xugang

Abstract

In recent years, the criticality of fore-aft vibrations induced by winds on wind turbine towers has increased; these vibrations are generally evaluated using equivalent fatigue load (EFL). This is the first study to adopt wind speed histories from large eddy simulations as input for dynamic analysis of wind turbines, and to evaluate EFL against real wind distribution. Tuned mass damper (TMD) and rotational inertial double tuned mass damper (RIDTMD) were employed to control these vibrations. Parametric analysis of the damper parameters was conducted. An innovative global optimization tool was developed based on a radial basis function neural network and genetic algorithm. Moreover, for the first time, GPU acceleration technologies were adopted to enable the optimizations of dampers through massive cases. Numerical results show that damper optimizations under real wind distributions are essential, and that optimized dampers reduce 44% EFL. The performance of RIDTMD is better than TMD but has a narrower system control bandwidth. The optimized dampers are significantly affected by wind speed; however, they are least affected by wind direction. The developed GPU-based codes can run 2001 times faster than the CPU-based ones, and the optimization tool can further reduce 85% computational time, which is open to other researchers.

Suggested Citation

  • Liu, Zhenqing & Wang, Yize & Nyangi, Patrice & Zhu, Zhiwen & Hua, Xugang, 2021. "Proposal of a novel GPU-accelerated lifetime optimization method for onshore wind turbine dampers under real wind distribution," Renewable Energy, Elsevier, vol. 168(C), pages 516-543.
    • Handle: RePEc:eee:renene:v:168:y:2021:i:c:p:516-543
    DOI: 10.1016/j.renene.2020.12.073
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    References listed on IDEAS

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    1. Rahman, Mahmudur & Ong, Zhi Chao & Chong, Wen Tong & Julai, Sabariah & Khoo, Shin Yee, 2015. "Performance enhancement of wind turbine systems with vibration control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 43-54.
    2. Evans, S.P. & Bradney, D.R. & Clausen, P.D., 2018. "Assessing the IEC simplified fatigue load equations for small wind turbine blades: How simple is too simple?," Renewable Energy, Elsevier, vol. 127(C), pages 24-31.
    3. Wan, Chunqiu & Wang, Jun & Yang, Geng & Gu, Huajie & Zhang, Xing, 2012. "Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy," Renewable Energy, Elsevier, vol. 48(C), pages 276-286.
    4. Silva, Paulo Augusto Strobel Freitas & Shinomiya, Léo Daiki & de Oliveira, Taygoara Felamingo & Vaz, Jerson Rogério Pinheiro & Amarante Mesquita, André Luiz & Brasil Junior, Antonio Cesar Pinho, 2017. "Analysis of cavitation for the optimized design of hydrokinetic turbines using BEM," Applied Energy, Elsevier, vol. 185(P2), pages 1281-1291.
    5. Luna, Julio & Falkenberg, Ole & Gros, Sébastien & Schild, Axel, 2020. "Wind turbine fatigue reduction based on economic-tracking NMPC with direct ANN fatigue estimation," Renewable Energy, Elsevier, vol. 147(P1), pages 1632-1641.
    6. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    7. Menon, Muraleekrishnan & Ponta, Fernando L., 2017. "Dynamic aeroelastic behavior of wind turbine rotors in rapid pitch-control actions," Renewable Energy, Elsevier, vol. 107(C), pages 327-339.
    8. Rezaei, Ramtin & Fromme, Paul & Duffour, Philippe, 2018. "Fatigue life sensitivity of monopile-supported offshore wind turbines to damping," Renewable Energy, Elsevier, vol. 123(C), pages 450-459.
    9. Ponta, Fernando L. & Otero, Alejandro D. & Lago, Lucas I. & Rajan, Anurag, 2016. "Effects of rotor deformation in wind-turbine performance: The Dynamic Rotor Deformation Blade Element Momentum model (DRD–BEM)," Renewable Energy, Elsevier, vol. 92(C), pages 157-170.
    10. Velarde, Joey & Kramhøft, Claus & Sørensen, John Dalsgaard, 2019. "Global sensitivity analysis of offshore wind turbine foundation fatigue loads," Renewable Energy, Elsevier, vol. 140(C), pages 177-189.
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    Cited by:

    1. Shi Liu & Yi Yang & Chengyuan Wang & Yuangang Tu & Zhenqing Liu, 2021. "Proposal of a Novel Mooring System Using Three-Bifurcated Mooring Lines for Spar-Type Off-Shore Wind Turbines," Energies, MDPI, vol. 14(24), pages 1-33, December.
    2. Wang, Yize & Liu, Zhenqing & Ma, Xueyun, 2023. "Improvement of tuned rolling cylinder damper for wind turbine tower vibration control considering real wind distribution," Renewable Energy, Elsevier, vol. 216(C).
    3. Zhang, Dongqin & Liu, Zhenqing & Li, Weipeng & Hu, Gang, 2023. "LES simulation study of wind turbine aerodynamic characteristics with fluid-structure interaction analysis considering blade and tower flexibility," Energy, Elsevier, vol. 282(C).

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