遗传算法在风力机风轮叶片优化设计中的应用

APPLICATION OF GENETIC ALGORITHMS TO HAWT ROTOR BLADES OPTIMIZATION

提出了风力机风轮叶片的优化设计模型,该模型考虑了风场风速的概率分布,以风力机年能量输出最大为设计目标。为加速搜索寻优过程并保证获得全局最优解,采用ECGA算法进行搜索寻优。与传统的遗传算法相比,ECGA搜索寻优速度更快,获得的结果更准确,也可以使用更小的种群,函数求值次数也更少。利用开发的优化设计程序,设计了1.3MW失速型风力机的叶片。与已有的1.3MW失速型风力机相比,设计结果显示了明显的优越性,说明该优化设计模型的有效性和实用性。

An optimization model for rotor blades of horizontal axis wind turbines （HAWT） was presented. It referred to the wind speed distribution function on the specific wind site, with the objective to satisfy the maximum annual energy output. To speed up the search process and guarantee to obtain the global optimal result, the extended compact genetic algorithm （ECGA） is used to carry on the search process. Compared with the simple genetic algorithm, ECGA runs much faster and can get more accurate results but needs a much smaller population size and fewer function evaluations. Using the developed optimization program, the blades of a 1.3 MW stall-regulated wind turbine were designed. Compared with an existing 1.3 MW stall-regulated wind turbine, the designed shows obvious advantages, which verifies effectiveness and functionalities of the optimization model.