• CN：11-2187/TH
• ISSN：0577-6686

• 交叉与前沿 •

基于多目标遗传算法的风力机叶片全局优化设计

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

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.