Wind energy has been developed and is widely used as a clean and renewable form of energy. Among the existing variety of wind turbines, variable-speed variable-pitch wind turbines have become popular owing to their variable output power capability. In this study, a hybrid control strategy is proposed to implement pitch angle control. A new nonlinear hybrid control approach based on the Adaptive Neuro-Fuzzy Inference System and fuzzy logic control is proposed to regulate the pitch angle and maintain the captured mechanical energy at the rated value. In the controller, the reference value of the pitch angle is predicted by the Adaptive Neuro-Fuzzy Inference System according to the wind speed and the blade tip speed ratio. A proposed fuzzy logic controller provides feedback based on the captured power to modify the pitch angle in real time. The effectiveness of the proposed hybrid pitch angle control method was verified on a 5 MW offshore wind turbine under two different wind conditions using MATLAB/Simulink. The simulation results showed that fluctuations in rotor speed were dramatically mitigated, and the captured mechanical power was always near the rated value as compared with the performance when using the Adaptive Neuro-Fuzzy Inference System alone. The variation rate of power was 0.18% when the proposed controller was employed, whereas it was 2.93% when only an Adaptive Neuro-Fuzzy Inference System was used.

风能已被开发并作为一种清洁和可再生能源被广泛使用。在现有的各种风力涡轮机中，变速变桨距风力涡轮机因其可变输出功率能力而受到欢迎。在这项研究中，提出了一种混合控制策略来实现桨距角控制。提出了一种基于自适应神经模糊推理系统和模糊逻辑控制的非线性混合控制方法来调节桨距角并将捕获的机械能保持在额定值。在控制器中，自适应神经模糊推理系统根据风速和叶尖速比预测桨距角的参考值。提出的模糊逻辑控制器根据捕获的功率提供反馈以实时修改桨距角。使用 MATLAB/Simulink 在两种不同风况下的 5 MW 海上风力涡轮机上验证了所提出的混合桨距角控制方法的有效性。仿真结果表明，与单独使用自适应神经模糊推理系统时的性能相比，转子速度的波动得到了显着缓解，并且捕获的机械功率始终接近额定值。当采用所提出的控制器时，功率变化率为 0.18%，而当仅使用自适应神经模糊推理系统时，功率变化率为 2.93%。与单独使用自适应神经模糊推理系统时的性能相比，捕获的机械功率始终接近额定值。当采用所提出的控制器时，功率变化率为 0.18%，而当仅使用自适应神经模糊推理系统时，功率变化率为 2.93%。与单独使用自适应神经模糊推理系统时的性能相比，捕获的机械功率始终接近额定值。当采用所提出的控制器时，功率变化率为 0.18%，而当仅使用自适应神经模糊推理系统时，功率变化率为 2.93%。