Icing diagnosis model for wind turbine blade based on feature optimization and 1D-convolutional neural network

Abstract

Wind turbines located in high humidity and high altitude areas are often accompanied by blade icing, which adverses the operating efficiency and even causes safety accidents. Early identification of blade icing will help improve the operating efficiency of the wind turbine. This paper proposes an icing diagnosis method for wind turbine blades based on feature optimization and the one-dimensional convolutional neural network (1D-CNN). First, feature optimization is achieved by feature selection and feature reconstruction. The XGBoost algorithm is used to calculate the importance of each feature and select the features comprehensively that reflect blade icing. Second, the important features related to blade icing are reconstructed by using the deviation principle to extract the deviation information of features accurately when blades ice. Finally, the features screened by XGBoost and the reconstructed features are combined into the final feature set as the input of the 1D-CNN, which takes the temporal and spatial characteristics of data into account, to diagnose the icing state of blades. The method is validated on the data set collected from a real wind farm. The experimental results show that the proposed icing diagnosis method for wind turbine blades is superior to the traditional deep learning methods. It is favorable to improve the efficiency of wind turbine operation and maintenance.