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Wind turbine blade geometry design based on multi-objective optimization using metaheuristics

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  • Vianna Neto, Júlio Xavier
  • Guerra Junior, Elci José
  • Moreno, Sinvaldo Rodrigues
  • Hultmann Ayala, Helon Vicente
  • Mariani, Viviana Cocco
  • Coelho, Leandro dos Santos

Abstract

The application of evolutionary algorithms to wind turbine blade design can be interesting, by reducing the number of aerodynamic-to-structural design loops in the conventional design process, hence reducing the design time and cost. Recent developments showed satisfactory results with this approach, mostly combining genetic algorithms with the blade element momentum theory. The general objective of the present work is to define and evaluate a design methodology for the rotor blade geometry in order to maximize the energy production of wind turbines and minimize the mass of the blade itself, using for that purpose stochastic multi-objective optimization methods. An optimization benchmark problem was proposed, which represents the wind conditions and present wind turbine concepts found in Brazil. A variable speed pitch-controlled 2.5 MW direct-drive synchronous generator turbine with a rotor diameter of 120 m was chosen as concept. Four different multi-objective evolutionary algorithms were selected for evaluation in solving this benchmark problem: Non-dominated Sorting Genetic Algorithm version II (NSGA-II), Quantum-inspired Multi-objective Evolutionary Algorithm (QMEA), Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multi-objective Optimization Differential Evolution Algorithm (MODE). Detailed analysis of the best compromise blade design showed that the output of the design methodology is feasible for manufacturing.

Suggested Citation

  • Vianna Neto, Júlio Xavier & Guerra Junior, Elci José & Moreno, Sinvaldo Rodrigues & Hultmann Ayala, Helon Vicente & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2018. "Wind turbine blade geometry design based on multi-objective optimization using metaheuristics," Energy, Elsevier, vol. 162(C), pages 645-658.
    • Handle: RePEc:eee:energy:v:162:y:2018:i:c:p:645-658
    DOI: 10.1016/j.energy.2018.07.186
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    References listed on IDEAS

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    Cited by:

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    2. Yin, Xiuxing & Zhao, Xiaowei & Lin, Jin & Karcanias, Aris, 2020. "Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations," Energy, Elsevier, vol. 202(C).
    3. Song, Dongran & Xu, Shanmin & Huang, Lingxiang & Xia, E. & Huang, Chaoneng & Yang, Jian & Hu, Yang & Fang, Fang, 2022. "Multi-site and multi-objective optimization for wind turbines based on the design of virtual representative wind farm," Energy, Elsevier, vol. 252(C).
    4. Moreno, Sinvaldo Rodrigues & Pierezan, Juliano & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2021. "Multi-objective lightning search algorithm applied to wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    5. Abbassi, Abdelkader & Abbassi, Rabeh & Heidari, Ali Asghar & Oliva, Diego & Chen, Huiling & Habib, Arslan & Jemli, Mohamed & Wang, Mingjing, 2020. "Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach," Energy, Elsevier, vol. 198(C).
    6. 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.
    7. Sun, ZhaoCheng & Li, Dong & Mao, YuFeng & Feng, Long & Zhang, Yue & Liu, Chao, 2022. "Anti-cavitation optimal design and experimental research on tidal turbines based on improved inverse BEM," Energy, Elsevier, vol. 239(PD).

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