大型风机的独立变桨控制方法

为了缓解风力发电机组由于风速扰动所造成的疲劳载荷，给出了一种基于RBF神经网络滑模独立变桨控制策略。通过分析风力机的基本特性，提出将RBF神经网络滑模功率控制单元和独立变桨控制单元相结合的控制方式。RBF神经网络滑模功率控制单元通过对发电机电磁转矩及桨叶桨距角的控制来平衡风力机的气动转矩，使风轮保持额度转速，实现稳定风电机组的输出功率的目的。而RBF神经网络独立变桨滑模控制单元通过实时微调风机桨距角，来优化功率控制单元的统一桨距角信号，实现缓解风机结构疲劳载荷的目的。最后，通过建立基于RBF神经网络滑模独立变桨控制的风力发电机组进行相应的仿真与实验，证明基于RBF神经网络功率控制和独立变桨滑模控制相结合的方法具有良好的控制效果，稳定风机输出功率的同时，极大地缓解风机的结构载荷，降低风力发电机组的维护成本。

Methods of individual pitch control for large wind turbine

In order to alleviate fatigue loads for wind turbines due to wind speed disturbances, this paper presents a sliding individual pitch control strategy based on RBF neural network. By analyzing the basic characteristics of the wind turbine, it proposes a control mode combining with RBF neural network sliding mode power control unit and individual pitch control unit. RBF neural network sliding mode power control unit balances the wind rotor via a pneumatic torque control of the generator electromagnetic torque and blade pitch angle, and keeps the amount of speed to achieve the purpose of stable output power wind turbines. RBF neural sliding network individual pitch control unit optimizes unified unit angle signal of power control through fine-tune real pitch angle, and achieves the purpose of relieving structure fatigue loads for wind turbine. Finally, corresponding simulation and experiments based on RBF neural network independent sliding pitch controlled wind turbines are established, it verifies that the proposed method has good control effect, achieves stability output power for large wind turbine, greatly eases structural loads , and reduces the maintenance costs of wind turbines.