Prediction of aerodynamic coefficients of iced conductors based on composite image and convolutional neural network

Abstract

The aerodynamic force of iced conductors is a key factor in the galloping study of transmission lines. At present, it is less efficient to obtain the aerodynamic coefficients of iced conductors by wind tunnel tests or numerical simulations. In this paper, an efficient method is proposed to predict the aerodynamic coefficients of iced conductors based on a convolutional neural network. The linear mapping relationships are constructed between the wind flow parameters and the shape image RGB matrix to generate composite images. Then the composite image and the convolutional neural network (CI–CNN) model is proposed. Due to the aerodynamic coefficients having a strong nonlinearity, the Fourier fitting function is used to describe the nonlinear characteristics of the aerodynamic coefficients changing with the attack angles. Then the Fourier composite image and convolutional neural network (FCI–CNN) model is proposed. The 1443 aerodynamic coefficients of crescent-shaped iced conductors from the wind tunnel test are taken as an example. The results show that both the FCI–CNN model and the CI–CNN model perform well during the estimation of the drag coefficient, especially the R2 calculated as 0.986 and 0.977, with MAE as 0.087 and 0.066, proving that the proposed method is effective. When predicting the lift coefficient, the R2 of the FCI–CNN model and the CI–CNN model are 0.931 and 0.838, respectively. So do the result in the prediction of the torque coefficient. Through the Den Hartog coefficient, the FCI–CNN model reflects the most important range of 170°–185° galloping wind attack angle compared to the wind tunnel test results. The FCI–CNN model gives better predictions than the CI–CNN model.