IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i1p101-d87895.html
   My bibliography  Save this article

Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades

Author

Listed:
  • Jie Zhu

    (College of Civil Engineering and Architecture, Jiaxing University, Jiaxing 314001, China)

  • Xin Cai

    (College of Mechanics and Materials, Hohai University, Nanjing 210098, China)

  • Rongrong Gu

    (Architecture Engineering Institute, Jinling Institute of Technology, Nanjing 211169, China)

Abstract

A procedure based on MATLAB combined with ANSYS is presented and utilized for the multi-objective aerodynamic and structural optimization of horizontal-axis wind turbine (HAWT) blades. In order to minimize the cost of energy (COE) and improve the overall performance of the blades, materials of carbon fiber reinforced plastic (CFRP) combined with glass fiber reinforced plastic (GFRP) are applied. The maximum annual energy production (AEP), the minimum blade mass and the minimum blade cost are taken as three objectives. Main aerodynamic and structural characteristics of the blades are employed as design variables. Various design requirements including strain, deflection, vibration and buckling limits are taken into account as constraints. To evaluate the aerodynamic performances and the structural behaviors, the blade element momentum (BEM) theory and the finite element method (FEM) are applied in the procedure. Moreover, the non-dominated sorting genetic algorithm (NSGA) II, which constitutes the core of the procedure, is adapted for the multi-objective optimization of the blades. To prove the efficiency and reliability of the procedure, a commercial 1.5 MW HAWT blade is used as a case study, and a set of trade-off solutions is obtained. Compared with the original scheme, the optimization results show great improvements for the overall performance of the blade.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:101-:d:87895
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/1/101/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/1/101/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhiqiang Yang & Minghui Yin & Yan Xu & Zhengyang Zhang & Yun Zou & Zhao Yang Dong, 2016. "A Multi-Point Method Considering the Maximum Power Point Tracking Dynamic Process for Aerodynamic Optimization of Variable-Speed Wind Turbine Blades," Energies, MDPI, vol. 9(6), pages 1-16, May.
    2. Jie Zhu & Xin Cai & Pan Pan & Rongrong Gu, 2014. "Multi-Objective Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using the Non-Dominated Sorting Genetic Algorithm II and Finite Element Method," Energies, MDPI, vol. 7(2), pages 1-15, February.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Yeo, Eng Jet & Kennedy, David M. & O'Rourke, Fergal, 2022. "Tidal current turbine blade optimisation with improved blade element momentum theory and a non-dominated sorting genetic algorithm," Energy, Elsevier, vol. 250(C).

    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. 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.
    2. 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.
    3. Escalera Mendoza, Alejandra S. & Griffith, D. Todd & Jeong, Michael & Qin, Chris & Loth, Eric & Phadnis, Mandar & Pao, Lucy & Selig, Michael S., 2023. "Aero-structural rapid screening of new design concepts for offshore wind turbines," Renewable Energy, Elsevier, vol. 219(P2).
    4. Chen, Z.J. & Stol, K.A. & Mace, B.R., 2017. "Wind turbine blade optimisation with individual pitch and trailing edge flap control," Renewable Energy, Elsevier, vol. 103(C), pages 750-765.
    5. Ozan Gözcü & Taeseong Kim & David Robert Verelst & Michael K. McWilliam, 2022. "Swept Blade Dynamic Investigations for a 100 kW Small Wind Turbine," Energies, MDPI, vol. 15(9), pages 1-22, April.
    6. Sedaghat, Ahmad & Hassanzadeh, Arash & Jamali, Jamaloddin & Mostafaeipour, Ali & Chen, Wei-Hsin, 2017. "Determination of rated wind speed for maximum annual energy production of variable speed wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 781-789.
    7. Alkhabbaz, Ali & Yang, Ho-Seong & Weerakoon, A.H Samitha & Lee, Young-Ho, 2021. "A novel linearization approach of chord and twist angle distribution for 10 kW horizontal axis wind turbine," Renewable Energy, Elsevier, vol. 178(C), pages 1398-1420.
    8. Zhiqiang Yang & Minghui Yin & Yan Xu & Yun Zou & Zhao Yang Dong & Qian Zhou, 2016. "Inverse Aerodynamic Optimization Considering Impacts of Design Tip Speed Ratio for Variable-Speed Wind Turbines," Energies, MDPI, vol. 9(12), pages 1-15, December.
    9. Bertašienė, Agnė & Azzopardi, Brian, 2015. "Synergies of Wind Turbine control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 336-342.
    10. Kyoungboo Yang, 2020. "Geometry Design Optimization of a Wind Turbine Blade Considering Effects on Aerodynamic Performance by Linearization," Energies, MDPI, vol. 13(9), pages 1-18, May.
    11. Barooni, M. & Ale Ali, N. & Ashuri, T., 2018. "An open-source comprehensive numerical model for dynamic response and loads analysis of floating offshore wind turbines," Energy, Elsevier, vol. 154(C), pages 442-454.
    12. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    13. Lin, Whei-Min & Hong, Chih-Ming & Cheng, Fu-Sheng, 2010. "On-line designed hybrid controller with adaptive observer for variable-speed wind generation system," Energy, Elsevier, vol. 35(7), pages 3022-3030.
    14. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
    15. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    16. Arkaitz Rabanal & Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Unai Elosegui, 2018. "MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms," Energies, MDPI, vol. 12(1), pages 1-19, December.
    17. Seo, Junwon & Pokhrel, Jharna & Hu, Jong Wan, 2022. "Multi-Hazard Fragility Analysis of Offshore Wind Turbine Portfolios using Surrogate Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    18. Sergiienko, N.Y. & da Silva, L.S.P. & Bachynski-Polić, E.E. & Cazzolato, B.S. & Arjomandi, M. & Ding, B., 2022. "Review of scaling laws applied to floating offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    19. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    20. Przemyslaw Baranski & Piotr Pietrzak, 2016. "Computational Effective Fault Detection by Means of Signature Functions," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-20, March.

    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:10:y:2017:i:1:p:101-:d:87895. 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.