Development of surrogate models for evaluating energy transfer quality of high-speed railway pantograph-catenary system using physics-based model and machine learning

High-speed railway pantograph-catenary system is the only energy transfer pathway to drive a train operation. Energy transfer quality deteriorates with the increasing train speed and harsh service environment, thereby quickly and accurately evaluating the energy transfer quality is very important to guarantee the normal operation of a train. In this study, firstly, the physics-based model to simulate the dynamic interaction of pantograph-catenary system is established and validated. Eleven input parameters involve the essential line design and train operation parameters, and the output parameters that are crucially responsible for energy transfer quality are obtained by feature extraction. Secondly, a sampling strategy is employed to construct the input sampling points, based on which the outputs are computed via physics-based model, then combining them the dataset is obtained. Thirdly, five tree-based classification surrogate models are developed and compared to assess the level of energy transfer quality. Finally, eight regression surrogate models are developed in replacing physics-based model to evaluate the essential values of energy transfer quality. It is found that the gradient boosting decision tree (GBDT)-based surrogate model is the optimal classification model and the multi-layer feed-forward deep neural network (MLF-DNN)-based surrogate model for the optimal regression model. The two surrogate models are expected to quickly find the optimal design parameters and improve the operation control of trains of high-speed railway for the purpose of enhancing the energy transfer quality if coupled with optimization procedure.