说明：  一个关于matlab优化工具箱GOT中的遗传算法函数（GA）和全局优化算法函数（Globalsearch）优化能力的小对比，前者寻优快，但是结果不稳定，在风电场协同有功出力优化上面甚至可能比不过传统的单个风机层面的优化。后者计算量大，耗时较长，但是每次计算结果稳定，较传统单台风机层面优化有少量的提升。Tips:1,本文风场的建模只是采用了PARK模型，实际风场的气动过程应该还要复杂一些，未经实际风场验证，权当是工程优化入门 2,单个风机层面的功率系数Cp参数来源于NREL 5MW模型，跟实际叶片气动性能有关，可靠性未知。3,这种风场级别的协同优化主要考虑尾流因而更加适用于地形较平坦的风电场。《基于风机运行参数协同设定的风电场有功出力优化》是部分由这个程序支撑的，欢迎交流。
(The classical operation strategy of a wind farm works with a principle that each wind turbine converting as much aerodynamic power as available the incoming airflow. But this does not guarantee that the power converted by the whole wind farm be a maximum due to the wake effect. Unlike the conventional operation, this paper proposes the collaborative setting of the operation point of each turbine so that the overall production of the wind farm is maximized. The optimization is performed by Matlab Global Optimization Toolbox(GOT) based on a PARK wake model accompany with the area-weighted quadratic sum method. The strategy was proved applicable in many cases by sensitivity analysis. As an important side effect, the proposed method also allows decreasing the added turbulence, hence the mechanical load produced by wakes. As a consequence, the overall wind farm availability is increased. )