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Multi-objective optimisation of horizontal axis wind turbine structure and energy production using aerofoil and blade properties as design variables

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  • Fischer, Gunter Reinald
  • Kipouros, Timoleon
  • Savill, Anthony Mark

Abstract

The design of wind turbine blades is a true multi-objective engineering task. The aerodynamic effectiveness of the turbine needs to be balanced with the system loads introduced by the rotor. Moreover the problem is not dependent on a single geometric property, but besides other parameters on a combination of aerofoil family and various blade functions. The aim of this paper is therefore to present a tool which can help designers to get a deeper insight into the complexity of the design space and to find a blade design which is likely to have a low cost of energy. For the research we use a Computational Blade Optimisation and Load Deflation Tool (CoBOLDT) to investigate the three extreme point designs obtained from a multi-objective optimisation of turbine thrust, annual energy production as well as mass for a horizontal axis wind turbine blade. The optimisation algorithm utilised is based on Multi-Objective Tabu Search which constitutes the core of CoBOLDT. The methodology is capable to parametrise the spanning aerofoils with two-dimensional Free Form Deformation and blade functions with two tangentially connected cubic splines. After geometry generation we use a panel code to create aerofoil polars and a stationary Blade Element Momentum code to evaluate turbine performance. Finally, the obtained loads are fed into a structural layout module to estimate the mass and stiffness of the current blade by means of a fully stressed design. For the presented test case we chose post optimisation analysis with parallel coordinates to reveal geometrical features of the extreme point designs and to select a compromise design from the Pareto set. The research revealed that a blade with a feasible laminate layout can be obtained, that can increase the energy capture and lower steady state systems loads. The reduced aerofoil camber and an increased L/D-ratio could be identified as the main drivers. This statement could not be made with other tools of the research community before.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:62:y:2014:i:c:p:506-515
    DOI: 10.1016/j.renene.2013.08.009
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    References listed on IDEAS

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    1. 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.
    2. Jaeggi, D.M. & Parks, G.T. & Kipouros, T. & Clarkson, P.J., 2008. "The development of a multi-objective Tabu Search algorithm for continuous optimisation problems," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1192-1212, March.
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    Cited by:

    1. Del Valle Carrasco, Arturo & Valles-Rosales, Delia J. & Mendez, Luis C. & Rodriguez, Manuel I., 2016. "A site-specific design of a fixed-pitch fixed-speed wind turbine blade for energy optimization using surrogate models," Renewable Energy, Elsevier, vol. 88(C), pages 112-119.
    2. Daróczy, László & Janiga, Gábor & Petrasch, Klaus & Webner, Michael & Thévenin, Dominique, 2015. "Comparative analysis of turbulence models for the aerodynamic simulation of H-Darrieus rotors," Energy, Elsevier, vol. 90(P1), pages 680-690.
    3. Khazar Hayat & Shafaqat Siddique & Tipu Sultan & Hafiz T. Ali & Fahed A. Aloufi & Riyadh F. Halawani, 2022. "Effect of Spar Design Optimization on the Mass and Cost of a Large-Scale Composite Wind Turbine Blade," Energies, MDPI, vol. 15(15), pages 1-17, August.
    4. Tahani, Mojtaba & Kavari, Ghazale & Masdari, Mehran & Mirhosseini, Mojtaba, 2017. "Aerodynamic design of horizontal axis wind turbine with innovative local linearization of chord and twist distributions," Energy, Elsevier, vol. 131(C), pages 78-91.
    5. 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.
    6. Liu, Wenyi, 2016. "Design and kinetic analysis of wind turbine blade-hub-tower coupled system," Renewable Energy, Elsevier, vol. 94(C), pages 547-557.
    7. Jia, Liangyue & Hao, Jia & Hall, John & Nejadkhaki, Hamid Khakpour & Wang, Guoxin & Yan, Yan & Sun, Mengyuan, 2021. "A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power," Energy, Elsevier, vol. 215(PA).
    8. 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.
    9. 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.
    10. Hashem, I. & Mohamed, M.H., 2018. "Aerodynamic performance enhancements of H-rotor Darrieus wind turbine," Energy, Elsevier, vol. 142(C), pages 531-545.
    11. Nejra Beganovic & Jackson G. Njiri & Dirk Söffker, 2018. "Reduction of Structural Loads in Wind Turbines Based on an Adapted Control Strategy Concerning Online Fatigue Damage Evaluation Models," Energies, MDPI, vol. 11(12), pages 1-15, December.
    12. Gao, Linyue & Zhang, Hui & Liu, Yongqian & Han, Shuang, 2015. "Effects of vortex generators on a blunt trailing-edge airfoil for wind turbines," Renewable Energy, Elsevier, vol. 76(C), pages 303-311.
    13. Yuquan Meng & Yuhang Yang & Haseung Chung & Pil-Ho Lee & Chenhui Shao, 2018. "Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review," Sustainability, MDPI, vol. 10(12), pages 1-28, December.
    14. Daróczy, László & Janiga, Gábor & Thévenin, Dominique, 2016. "Analysis of the performance of a H-Darrieus rotor under uncertainty using Polynomial Chaos Expansion," Energy, Elsevier, vol. 113(C), pages 399-412.
    15. Bertašienė, Agnė & Azzopardi, Brian, 2015. "Synergies of Wind Turbine control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 336-342.
    16. Abdelsalam, Ali M. & El-Askary, W.A. & Kotb, M.A. & Sakr, I.M., 2021. "Experimental study on small scale horizontal axis wind turbine of analytically-optimized blade with linearized chord twist angle profile," Energy, Elsevier, vol. 216(C).
    17. 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.
    18. Rodrigues, S.S. & Marta, A.C., 2019. "On addressing wind turbine noise with after-market shape blade add-ons," Renewable Energy, Elsevier, vol. 140(C), pages 602-614.
    19. Sessarego, Matias & Feng, Ju & Ramos-García, Néstor & Horcas, Sergio González, 2020. "Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow," Renewable Energy, Elsevier, vol. 146(C), pages 1524-1535.

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