A feasibility study on applying meta-heuristic optimization and Gaussian process regression for predicting the performance of pantograph-catenary system

As the pantograph-catenary system provides electric energy for high-speed trains, it is vital to evaluate the contact force (CF) between pantograph and catenary for stable energy supply. The magnitude and variation range of CF determines the quality of current receiving and safe operation of the tra...

Full description

Saved in:
Bibliographic Details
Published in:Acta mechanica Sinica 2024, Vol.40 (1), Article 523282
Main Authors: Zhang, Mohan, Yin, Bo, Sun, Zhenxu, Bai, Ye, Yang, Guowei
Format: Article
Language:English
Subjects:
Catenaries
Classical and Continuum Physics
Computational Intelligence
Computer simulation
Contact force
Electric contacts
Energy transfer
Engineering
Engineering Fluid Dynamics
Feasibility studies
Finite element method
Gaussian process
Heuristic
Heuristic methods
High speed rail
Mathematical models
Optimization
Pantographs
Performance prediction
Preliminary designs
Regression
Research Paper
Theoretical and Applied Mechanics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As the pantograph-catenary system provides electric energy for high-speed trains, it is vital to evaluate the contact force (CF) between pantograph and catenary for stable energy supply. The magnitude and variation range of CF determines the quality of current receiving and safe operation of the train. Therefore, a rapid and accurate prediction of CF is of great significance. However, collecting CF data through experiments is challenging, and obtaining timely results using numerical simulations is not always feasible. In this study, we propose an efficient simulation-based surrogate approach based on Gaussian process regression (GPR), combined with meta-heuristic optimization, to predict key parameters of pantograph-catenary system, which are responsible for the energy transfer quality. Firstly, a pantograph-catenary model is established and validated using finite element method (FEM), which serves to generate training and test data. Secondly, Gaussian process regression is utilized for estimation. A new developed meta-heuristic optimization, i.e., binary hunger game search (HGS), is applied on feature selection. To enhance the performance of HGS, chaos mechanism is embedded, resulting in Chaos-HGS GPR (CHGS-GPR). Finally, the predictive results of CHGS-GPR are evaluated. It is found that the proposed CHGS-GPR provides rather accurate prediction for the mean value of CF, and can be extended to the preliminary design of railway lines, real-time evaluation, and control of train operations.
ISSN:0567-7718
1614-3116
DOI:10.1007/s10409-023-23282-x