Risk assessment models of power transmission lines undergoing heavy ice at mountain zones based on numerical model and machine learning

Abstract

To deliver the cleaner energy, such as water, wind and solar power, from the remote mountainous areas to the power load areas, overhead power transmission lines are widely employed around the world. It is not an easy job to guarantee the safety of the long distance power transmission with hundreds or even thousands of kilometers of lines, especially crossing heavy ice zones at mountains. In this work, a numerical model to simulate the static and dynamic responses of transmission lines undergoing heavy ice is proposed and validated. Using the model, parameter study under different line sections depending on the mountain terrains, height difference and tension in conductors is carried out. It is obtained that the ice-induced dynamic characteristics of transmission lines at mountain zones are highly different with those of traditional design standard, with more complicated and dangerous. Therefore, to assess the risk level of transmission lines at mountain zones, the numerical model is used to gather the big dataset. Then, five machine learning-based models, including three tree-based and two artificial neural network (ANN)-based models, are developed and compared to find the optimal one. It is found that the multi-layer feed-forward deep ANN (MLF-DNN)-based model behaves optimal with the best evaluation scores. Based on the models, the predominant input parameters to affect the risk level are obtained and some suggestions to improve the ice resistance for the design and operation of transmission lines at mountain zones are proposed.