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Wind Turbine Design: Multi-Objective Optimization

In: Wind Turbines - Design, Control and Applications

Author

Listed:
  • Adam Chehouri
  • Rafic Younes
  • Jean Perron

Abstract

Within the last 20 years, wind turbines have reached matured and the growing worldwide wind energy market will allow further improvements. In the recent decades, the numbers of research papers that have applied optimization techniques in the attempt to obtain an optimal design have increased. The main target of manufacturers has been to minimize the cost of energy of wind turbines in order to compete with fossil-fuel sources. Therefore, it has been argued that it is more stimulating to evaluate the wind turbine design as an optimization problem consisting of more than one objective. Using multi-objective optimization algorithms, the designers are able to identify a trade-off curve called Pareto front that reveals the weaknesses, anomalies and rewards of certain targets. In this chapter, we present the fundamental principles of multi-objective optimization in wind turbine design and solve a classic multi-objective wind turbine optimization problem using a genetic algorithm.

Suggested Citation

  • Adam Chehouri & Rafic Younes & Jean Perron, 2016. "Wind Turbine Design: Multi-Objective Optimization," Chapters, in: Abdel Ghani Aissaoui & Ahmed Tahour (ed.), Wind Turbines - Design, Control and Applications, IntechOpen.
    • Handle: RePEc:ito:pchaps:102515
    DOI: 10.5772/63481
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    File URL: https://www.intechopen.com/chapters/50998
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    Citations

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    Cited by:

    1. Shaoying Li & Zhongquan Qu, 2021. "Optimized Design of Structure of High-Bending-Rigidity Circular Tube," Sustainability, MDPI, vol. 13(8), pages 1-23, April.
    2. Joaquín Bautista-Valhondo & Lluís Batet & Manuel Mateo, 2020. "Minimizing the Standard Deviation of the Thermal Load in the Spent Nuclear Fuel Cask Loading Problem," Energies, MDPI, vol. 13(18), pages 1-26, September.

    More about this item

    Keywords

    wind turbine design; optimization; multi-objective; genetic algorithm; Pareto front;
    All these keywords.

    JEL classification:

    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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