基于粒子群算法的海上风机叶片优化算法研究

摘要

An optimization method for offshore wind turbine blades based on particle swarm optimization PSO) is presented.The blades are the core component of the wind turbine,and the power of the blade varies significantly in different sea conditions which leads to the reduction in the efficiency of wind turbine blades.Improved particle swarm optimization is introduced in this paper,and the power of the blade calculated by the corrected blade element momentum theory (BEM) is defined as the fitness function.The chord length and the twist angle distribution along the span wise of the blade are optimized in specific wind conditions to improve aerodynamic efficiency.Finally,the 5MW offshore wind turbine blades provided by the United States Renewable Energy Laboratory is optimized according to the wind conditions in East China Sea,and the average power of the optimized blade is increased by 6.7 ％.The result shows that the method is efficient in aerodynamic design of wind turbine blades.%提出了一种针对不同风场条件提高海上风力发电机叶片气动效率的方法.风力发电机叶片是风机效率的核心部件,叶片在不同海上风场条件下功率差别很大,这导致了风机效率的降低.通过引入改进粒子群优化算法(PSO),基于修正动量叶素理论(BEM),对叶片沿展向的弦长分布和扭转角进行针对特定风场条件的气动优化.以美国再生能源实验室提供的5MW海上风机叶片作为算例,结合我国东海风场条件,叶片优化后平均功率提高了6.7％,取得了理想效果.结果表明,该优化方法具有较高的气动优化效率.