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Reliability-based design optimisation framework for wind turbine towers

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  • Al-Sanad, Shaikha
  • Wang, Lin
  • Parol, Jafarali
  • Kolios, Athanasios

Abstract

The current design of wind turbine (WT) towers is generally based on the partial safety factor (PSF) method, which treats uncertain variables deterministically and applies PSFs to account for uncertainties. This simplification in the design process leads to either over-engineered or under-engineered designs most of the time. In this study, a reliability-based design optimisation (RBDO) framework for WT towers is developed, accurately taking account of uncertainties in wind loads and material properties. A parametric finite element analysis (FEA) model for WT towers is developed, taking account of stochastic variables. After validation, it is then combined with response surface method and first order reliability method to develop a reliability assessment model. Five limit states are considered, i.e. ultimate, fatigue, buckling, modal frequency and tower top rotation. The reliability assessment model is further integrated with a genetic algorithm (GA) to develop a RBDO framework. The RBDO framework has been applied to a typical 2.0 MW onshore WT tower currently installed in a representative location in Middle East. The results demonstrate that the proposed RBDO framework can effectively and accurately achieve an optimal design of WT towers to meet target reliability.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:167:y:2021:i:c:p:942-953
    DOI: 10.1016/j.renene.2020.12.022
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    References listed on IDEAS

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    1. Zhu, Lei & Li, Huaqi & Chen, Sen & Tian, Xiaoyan & Kang, Xiaoya & Jiang, Xinbiao & Qiu, Suizheng, 2020. "Optimization analysis of a segmented thermoelectric generator based on genetic algorithm," Renewable Energy, Elsevier, vol. 156(C), pages 710-718.
    2. Ram, Krishnil R. & Lal, Sunil P. & Ahmed, M. Rafiuddin, 2019. "Design and optimization of airfoils and a 20 kW wind turbine using multi-objective genetic algorithm and HARP_Opt code," Renewable Energy, Elsevier, vol. 144(C), pages 56-67.
    3. Gentils, Theo & Wang, Lin & Kolios, Athanasios, 2017. "Integrated structural optimisation of offshore wind turbine support structures based on finite element analysis and genetic algorithm," Applied Energy, Elsevier, vol. 199(C), pages 187-204.
    4. 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.
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    Cited by:

    1. Charis J. Gantes & Maria Villi Billi & Mahmut Güldogan & Semih Gül, 2021. "A Novel Tripod Concept for Onshore Wind Turbine Towers," Energies, MDPI, vol. 14(18), pages 1-25, September.
    2. Meng, Debiao & Yang, Shiyuan & Jesus, Abílio M.P. de & Zhu, Shun-Peng, 2023. "A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower," Renewable Energy, Elsevier, vol. 203(C), pages 407-420.
    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. 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.
    5. Jannie Sønderkær Nielsen & Henrik Stensgaard Toft & Gustavo Oliveira Violato, 2023. "Risk-Based Assessment of the Reliability Level for Extreme Limit States in IEC 61400-1," Energies, MDPI, vol. 16(4), pages 1-15, February.

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