• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2015, Vol. 51 ›› Issue (14): 192-198.doi: 10.3901/JME.2015.14.192

• 交叉与前沿 • 上一篇    下一篇

基于多目标遗传算法的风力机叶片全局优化设计

杨阳1, 李春1,2, 缪维跑1, 叶舟1,2   

  1. 1.上海理工大学能源与动力工程学院 上海 200093
    2.上海理工大学上海市动力工程多相流动与传热重点实验室 上海 200093
  • 出版日期:2015-07-20 发布日期:2015-07-20

Global Optimal Design of Wind Turbines Blade Based on Multi-object Genetic Algorithm

YANG Yang1, LI Chun1,2, MIAO Weipao1, YE Zhou1,2   

  1. 1.School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093
    2.Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093
  • Online:2015-07-20 Published:2015-07-20
  • Supported by:
    国家自然科学基金(51165023)和兰州理工大学红柳青年培养计划 (Q201202)资助项目

摘要: 风力机叶片设计的目标多样性使得传统单一目标设计方法无法满足设计要求,大型风力机的高发电量与大负载之间的矛盾必须得到平衡。为此,以年发电量最大和叶片质量最轻为优化设计目标,通过多目标遗传算法设计5 MW大型风力机叶片,得到Pareto分布优化解集。与美国可再生能源实验室(National Renewable Energy Laboratory, NREL)设计的5 MW风力机叶片比较,结果表明,Pareto优化解集均一定程度优于参考叶片,年发电量最大提高量为3.3%,最大质量减少量为8.7%,其中优化设计叶片2在质量降低3.8%的基础上,提高了3%的年发电量,达到了优化设计的目的。优化设计叶片额定风况下推力系数和叶根弯矩均更小,最大功率系数更大。变风况下功率特性与参考叶片相差不大,低风速下设计叶片输出功率略高,推力系数更小。

关键词: Pareto解集, 多目标优化, 风力机叶片, 遗传算法

Abstract: Conventional single-object design methods which have been adopted previously should not meet the requirements universally because of the diversity of wind turbines blade design-objectives. Therefore, the contradiction between high capacity and heavy load of large-scale wind turbines must be balanced. In order to solve the mention challenge, design blades of a 5 MW wind turbine with taking the maximum annual energy production(AEP) and the minimum blade mass as the optimization objectives based on multi-objective genetic algorithm, and get the Pareto-optimal solutions. Comparing with the blade of National Renewable Energy Laboratory(NREL) 5 MW wind turbine, the results show that the Pareto-optimal solutions are better than the reference blade. The maximum increased amount of AEP achieves 3.3% and the decrement reaches 8.7%. The design purpose is reached showing by one of the Pareto sets with 3% higher annual energy production and mass reduction about 3.8%. Optimal blades have smaller thrust coefficient and root bending moment with higher maximum coefficient of power in rated wind speed, the power characteristics are little different from the reference blade in design wind-speed range, but these have higher output power and smaller thrust coefficient in low wind speeds.

Key words: genetic algorithm, multi-object optimization, Pareto solutions, wind turbine blade

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