摘要
以带有尾缘襟翼的NREL 5 MW参考风力机为研究对象,综合考虑非定常气动力、气动阻尼和弯扭耦合等因素,建立了改进的智能叶片气弹模型,并与FAST平台进行仿真对比。基于径向基函数(RBF)神经网络自适应比例、积分、微分(PID)方法设计了尾缘襟翼主动控制器,在标准湍流风况下对叶尖偏移量进行仿真控制。结果表明:改进气弹模型的准确度较高;尾缘襟翼主动控制方法可有效减小叶尖偏移量的波动。
Taking the NREL 5 MW reference wind turbine with trailing edge flaps as an object of study, an improved aeroelastic model was established for the smart blade considering the unsteady aerodynamics, aerodynamic damping and bend-twist coupling, of which the simulation results were compared with that of FAST platform. Based on the adaptive PID of RBF neural network, an active controller was designed for the trailing edge flap to control the deflection of blade tips under standard turbulent wind conditions. Results show that the accuracy of the improved aeroelastic model is relatively high;the active controller for the trailing edge flap can effectively reduce the fluctuation of the blade tip deflection.
作者
张文广
王媛媛
刘瑞杰
沈炀智
ZHANG Wenguang;WANG Yuanyuan;LIU Ruijie;SHEN Yangzhi(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)
出处
《动力工程学报》
CAS
CSCD
北大核心
2019年第9期758-764,776,共8页
Journal of Chinese Society of Power Engineering
基金
国家重点研发计划资助项目(2017YFB0602105)
北京市共建资助项目(GJ2017006)
中央高校基本科研业务费专项资金资助项目(2018ZD05)
关键词
风力机
尾缘襟翼
气动阻尼
弯扭耦合
RBF神经网络
自适应PID
wind turbine
trailing edge flap
aerodynamic damping
bend-twist coupling
RBF neural net work
adaptive PID
