基于模态频率的风力机叶片覆冰检测方法

DETECTION OF ICE ACCRETION ON WIND TURINE BLADE ON MODAL FREQUENCY

目前,风力机叶片覆冰后通常使用加热系统除冰,其消耗的能源是发电机组1%~4%的年发电量。本文通过试验模态调校叶片有限元模型,用有限元模型的仿真模态得出不同位置的覆冰厚度和固有频率对应关系方程,用这些方程生成随机样本训练BP神经网络模型,建立以固有频率为输入,以覆冰厚度为输出的非线性关系,以实现覆冰状态的检测。研究表明,通过叶片的力锤激励模态试验结果,调整叶片模型参数,优化后的叶片覆冰中空实体三维模型前三阶固有频率与试验值误差在2%以内。通过BP神经网络建模和训练,模型检测覆冰厚度的结果与实际值相对误差率平均值为8.83%,叶尖处误差最小,叶根处误差最大,相对误差率随着冰层厚度的增加而降低。训练好的BP神经网络模型可以基本实现覆冰的位置和厚度信息检测,为加热系统精确加热位置和加热时间、降低能源消耗提供理论依据。

At present,the wind turbine blades are usually de-iced using a heating system after ice coating,and the energy consumption is about 1%~4%of the annual power generation of the generator set.In this paper,the finite element model is adjusted by experimental modal results,and the equations of the corresponding relationship between the thickness of the ice coating and the natural frequency at different positions are obtained by the simulation modes of the finite element model.These equations are used to generate random samples to train the BP neural network model.The frequency is the input,and the thickness of the ice is the nonlinear relationship of the output to realize the detection of the ice status.The research shows that through the results of the modal test of the blade′s force hammer excitation,the parameters of the blade model are adjusted,and the error between the first three-order natural frequency and the test value of the optimized three-dimensional model of the bladed iced hollow solid is within 2%.Through BP neural network modeling and training,the average error rate of the results of the model detection ice thickness and the actual value is 8.83%,the error at the blade tip is the smallest,and the error at the blade root is the largest,the relative error rate decreased with the increase of the ice thickness.The trained BP neural network model can basically realize the detection of ice coating position and thickness information,and provide a theoretical basis for the precise heating position and heating time of the heating system and reducing energy consumption.