Predicting the Dynamic Response of Transmission Tower–Line Systems Under Wind–Rain Loads

Abstract

This study, based on existing research on the dynamic response of transmission tower–line systems under wind and rain loads, proposes a method for predicting these responses using the TimesNet deep learning surrogate model. Initially, a numerical model of the tower–line system is developed to generate dynamic response time series data under the influence of wind velocity and rainfall forces. Wind velocity and precipitation intensity are used as inputs for the surrogate model, with the tower’s maximum displacement and the highest tension in the line serving as the corresponding outputs. Afterward, the fast Fourier transform (FFT) is used to transform the original one-dimensional input signals into their corresponding two-dimensional representations. Feature extraction is then performed using an Inception module with 2D convolutional kernels of varying sizes. Finally, based on the amplitude-weighted information obtained through the FFT, the two-dimensional tensors are transformed back into one-dimensional output signals. The experimental results show that the proposed surrogate model provides highly accurate dynamic response predictions, even under complex conditions involving the interaction between transmission towers and lines, as well as the combined effects of wind and rainfall.