基于时序上采样卷积神经网络的风机叶片结冰检测

Icing detection of wind turbine blade based on the time-dimensional upsampling convolutional neural network

风电机组叶片结冰检测对风电机组的安全性、可靠性以及经济性具有非常重要的现实意义.针对风电机组运行观测数据的非平衡和单点无时序性问题,提出一种基于过采样和时序上采样卷积神经网络的风机叶片结冰检测方法.首先,采用数据自适应综合过采样算法对原始非平衡数据集进行重采样,实现对非平衡数据集的均衡;然后,提出并构建一种时序上采样卷积神经网络模型,将原始单点向量型数据进行重构并上采样为二维网格型数据,同时将其自动映射成为稀疏的特征表示,实现准确的风机叶片结冰检测功能;最后,将所提出方法在真实风场采集的数据集上进行验证.实验结果表明,所提出的风机叶片结冰检测方法在数据集非平衡且采集条件有限(单点无时序性数据)的情况下,具有一定的有效性、稳定性和可行性.

The icing detection of the wind turbine blade has very important practical significance for the safety,reliability and economy of wind turbines.Aimed at the problem of imbalanced and single-time-point non-sequentiality of wind turbine operating observation data,a method is proposed based on the oversampling and the time-dimensional upsampling convolutional neural network model.Firstly,the adaptive synthetic algorithm is applied to original dataset to achieve the balance of the imbalanced dataset.Then,a time-dimensional upsampling convolutional neural network model is proposed and constructed.On one hand,the model can reconstruct and upsample the original single-time-point vector data into the two-dimensional grid data.On the other hand,it can automatically map the data into a sparse feature representation,to achieve an accurate icing detection of the wind turbine blade.Finally,the method is verified on a dataset collected from a real wind farm,and the experimental results show that the proposed icing detection method of the wind turbine blade is effective,stable,and feasible when the dataset is imbalanced and limited.