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System design of a wind turbine using a multi-level optimization approach

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  • Maki, Kevin
  • Sbragio, Ricardo
  • Vlahopoulos, Nickolas

Abstract

In this paper a multi-level system design (MLS) algorithm is presented and utilized for a wind turbine system analysis. The MLS guides the decision making process for designing a complex system where many alternatives and many mutually competing objectives and disciplines need to be considered and evaluated. Mathematical relationships between the design variables and the multiple discipline performance objectives are developed adaptively as the various design considerations are evaluated and as the design is being evolved. These relationships are employed for rewarding performance improvement during the decision making process by allocating more resources and influence to the disciplines which exhibit the improvement. Simulation tools developed by the National Renewable Energy Laboratory (NREL) are employed in the wind turbine design analysis. The Cost Of Energy (COE) comprises the overall system level objective, while performance improvements at two technical design disciplines are pursued at the same time. The optimal design of the blade geometry for maximum Annual Energy Production (AEP), and the structural design of the blade for minimum bending moment at the root of the blade comprise the two technical design disciplines. Scalar metamodels are developed for linking the design variables with the performance metrics associated with the design of the blade geometry. Main characteristics of the wind turbine, namely, the rotor diameter, the rotational speed, the maximum rated power, the hub height, the structural characteristics of the blade, and the geometric characteristics of the blade (distribution of thickness, twist angle, and chord) are employed as design variables for the overall design analysis. The optimization results and the physical insight which can be gained through a sensitivity analysis for the optimal configuration are presented and discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:43:y:2012:i:c:p:101-110
    DOI: 10.1016/j.renene.2011.11.027
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    References listed on IDEAS

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    1. 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.
    2. 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).
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    5. Jung-Tae Lee & Hyun-Goo Kim & Yong-Heack Kang & Jin-Young Kim, 2019. "Determining the Optimized Hub Height of Wind Turbine Using the Wind Resource Map of South Korea," Energies, MDPI, vol. 12(15), pages 1-13, July.
    6. Jenn-Jong Shieh & Kuo-Ing Hwu & Sheng-Ju Chen, 2023. "Perspective of Voltage-Fed Single-Phase Multilevel DC-AC Inverters," Energies, MDPI, vol. 16(2), pages 1-22, January.
    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. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    9. Giallanza, A. & Porretto, M. & Cannizzaro, L. & Marannano, G., 2017. "Analysis of the maximization of wind turbine energy yield using a continuously variable transmission system," Renewable Energy, Elsevier, vol. 102(PB), pages 481-486.
    10. 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.
    11. 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).
    12. Bertašienė, Agnė & Azzopardi, Brian, 2015. "Synergies of Wind Turbine control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 336-342.
    13. Ulas Eminoglu & Saffet Ayasun, 2014. "Modeling and Design Optimization of Variable-Speed Wind Turbine Systems," Energies, MDPI, vol. 7(1), pages 1-18, January.
    14. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
    15. 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.
    16. 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.
    17. 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.
    18. 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.

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