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Multi-objective aerodynamic and structural integrated optimization design of wind turbines at the system level through a coupled blade-tower model

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  • Zhu, Jie
  • Zhou, Zhong
  • Cai, Xin

Abstract

This paper presents a method for multi-objective optimization design of wind turbines at the system level through a coupled blade-tower model, aiming at finding the coupling effects between blade and tower and improving the wind turbines’ performances. The formulation and implementation that enable the aerodynamic and structural integrated design of the blade and tower simultaneously are detailed. The maximum annual energy production (AEP) and the minimum wind turbine mass are taken as two conflicted objectives. Main aerodynamic and structural parameters of the blade and tower are employed as design variables. Various design requirements including stress, strain, deflection, vibration and buckling limits are considered as constraints. The blade element momentum (BEM) theory combined with the finite element method (FEM) are applied to evaluate the aerodynamic performances and structural behaviors of the turbine. Moreover, the non-dominated sorting genetic algorithm (NSGA) II is used to achieve the best trade-off solutions between the objectives. To show the efficiency and reliability of the method, a commercial 1.5 MW wind turbine is used as a case study. Satisfactory results that can both increase the AEP and decrease the mass are obtained, which are superior to the results achieved by optimizing the blade and tower separately.

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  • Zhu, Jie & Zhou, Zhong & Cai, Xin, 2020. "Multi-objective aerodynamic and structural integrated optimization design of wind turbines at the system level through a coupled blade-tower model," Renewable Energy, Elsevier, vol. 150(C), pages 523-537.
  • Handle: RePEc:eee:renene:v:150:y:2020:i:c:p:523-537
    DOI: 10.1016/j.renene.2020.01.013
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    References listed on IDEAS

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    1. Jie Zhu & Xin Cai & Rongrong Gu, 2017. "Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades," Energies, MDPI, vol. 10(1), pages 1-18, January.
    2. Wang, Long & Wang, Tongguang & Wu, Jianghai & Chen, Guoping, 2017. "Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design," Energy, Elsevier, vol. 120(C), pages 346-361.
    3. Bai, Chi-Jeng & Wang, Wei-Cheng, 2016. "Review of computational and experimental approaches to analysis of aerodynamic performance in horizontal-axis wind turbines (HAWTs)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 506-519.
    4. Liu, Wenyi, 2016. "Design and kinetic analysis of wind turbine blade-hub-tower coupled system," Renewable Energy, Elsevier, vol. 94(C), pages 547-557.
    5. Jie Zhu & Xin Cai & Rongrong Gu, 2016. "Aerodynamic and Structural Integrated Optimization Design of Horizontal-Axis Wind Turbine Blades," Energies, MDPI, vol. 9(2), pages 1-18, January.
    6. Yurdusev, M.A. & Ata, R. & Çetin, N.S., 2006. "Assessment of optimum tip speed ratio in wind turbines using artificial neural networks," Energy, Elsevier, vol. 31(12), pages 2153-2161.
    7. Ashuri, T. & Zaaijer, M.B. & Martins, J.R.R.A. & van Bussel, G.J.W. & van Kuik, G.A.M., 2014. "Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy," Renewable Energy, Elsevier, vol. 68(C), pages 893-905.
    8. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    9. Fischer, Gunter Reinald & Kipouros, Timoleon & Savill, Anthony Mark, 2014. "Multi-objective optimisation of horizontal axis wind turbine structure and energy production using aerofoil and blade properties as design variables," Renewable Energy, Elsevier, vol. 62(C), pages 506-515.
    10. Maki, Kevin & Sbragio, Ricardo & Vlahopoulos, Nickolas, 2012. "System design of a wind turbine using a multi-level optimization approach," Renewable Energy, Elsevier, vol. 43(C), pages 101-110.
    11. Vasel-Be-Hagh, Ahmadreza & Archer, Cristina L., 2017. "Wind farm hub height optimization," Applied Energy, Elsevier, vol. 195(C), pages 905-921.
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    Cited by:

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    2. Al-Sanad, Shaikha & Wang, Lin & Parol, Jafarali & Kolios, Athanasios, 2021. "Reliability-based design optimisation framework for wind turbine towers," Renewable Energy, Elsevier, vol. 167(C), pages 942-953.
    3. Dan Li & Hongbing Bao & Ning Zhao, 2023. "Research of Turbine Tower Optimization Based on Criterion Method," Energies, MDPI, vol. 16(2), pages 1-17, January.
    4. Zuo, Haoran & Bi, Kaiming & Hao, Hong & Xin, Yu & Li, Jun & Li, Chao, 2020. "Fragility analyses of offshore wind turbines subjected to aerodynamic and sea wave loadings," Renewable Energy, Elsevier, vol. 160(C), pages 1269-1282.

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