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Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades

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Abstract

The non-dominated sorting genetic algorithm (NSGA) is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multiobjective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, P. R. China

    Long Wang  (王 珑), Tong-guang Wang  (王同光) & Yuan Luo  (罗 源)

Authors
  1. Long Wang  (王 珑)
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  2. Tong-guang Wang  (王同光)
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  3. Yuan Luo  (罗 源)
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Corresponding author

Correspondence to Long Wang  (王 珑).

Additional information

Communicated by Wen-rui HU

Project supported by the National Basic Research Program of China (973 Program) (No. 2007CB714600)

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Wang, L., Wang, Tg. & Luo, Y. Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades. Appl. Math. Mech.-Engl. Ed. 32, 739–748 (2011). https://doi.org/10.1007/s10483-011-1453-x

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  • DOI: https://doi.org/10.1007/s10483-011-1453-x

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Chinese Library Classification

2010 Mathematics Subject Classification

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