针对复杂地形下风资源的异质性和不稳定性所引发的风电机组频繁偏航问题，提出了一种基于风速分段与多目标优化的偏航控制策略。首先，深入分析风电场的风资源特性及偏航行为，确定了适用于偏航控制的风速区间划分方案，从而确保机组在不同风速条件下能够灵活调整，以实现最佳运行效率。然后，通过分析偏航系统的发电量和偏航轴承的疲劳寿命，构建以综合经济效益与偏航次数为优化目标的偏航控制模型，并应用快速非支配排序遗传算法（nondominated sorting genetic algorithm Ⅱ，NSGA-Ⅱ）对模型中的控制参数进行协同优化。最后，通过灵敏度分析法确定各风速区间的最优控制参数。仿真结果表明：优化后的偏航控制策略在各风速区间均能充分发挥其优势，合理平衡了综合经济效益与偏航次数之间的关系，尤其是在低风速条件下，显著减少了不必要的偏航次数，从而有效提升了风电机组的整体经济效益。

A yaw control strategy based on wind speed segmentation and multi-objective optimization was proposed to address the frequent yawing issues caused by the heterogeneity and instability of wind resources in complex terrains. First, a detailed analysis of the wind resource characteristics and yaw behavior of the wind farm was conducted to determine an appropriate wind speed segmentation scheme for yaw control, ensuring that the turbines can adjust flexibly under different wind speed conditions to achieve optimal operating efficiency. Next, by analyzing the power generation capacity of the yaw system and the fatigue life of the yaw bearing, a yaw control model was constructed with integrated economic benefits and yaw frequency as the optimization objectives. The nondominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ) was then applied to perform a collaborative optimization of the control parameters in the model. Finally, sensitivity analysis was used to determine the optimal control parameters for each wind speed range. Simulation results show that the optimized yaw control strategy fully leverages its advantages in all wind speed ranges, reasonably balancing the relationship between economic benefits and yaw frequency. Particularly under low wind speed conditions, unnecessary yaw actions are significantly reduced, thereby effectively improving the overall economic efficiency of the wind turbines.