应用元启发式优化和高斯过程回归预测受电弓-接触网系统性能的可行性研究

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

张莫晗 ¹银波 ¹孙振旭 ¹白夜 ²杨国伟¹

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作者信息

1. Key Laboratory for Mechanics in Fluid Solid Coupling Systems,Institute of Mechanics,Chinese Academy of Sciences,Beijing 100190,China;School of Engineering Science,University of Chinese Academy of Sciences,Beijing 100049,China
2. Locomotive & Car Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;Beijing Zongheng Electro-Mechanical Technology Co.,Ltd.,Beijing 100094,China
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摘要

受电弓接触网系统为高速列车提供电能,正确评估受电弓与接触网之间的接触力(CF)对于稳定供电至关重要.CF的大小和变化范围决定了列车受流质量和安全运行.因此,快速、准确地预测CF具有重要意义.然而,通过实验收集CF数据具有挑战性,并且通过数值模拟获得及时结果并不总是可行的.在本研究中,我们提出了一种结合元启发式优化和高斯过程回归的高效的代理模型方法,来预测受电弓接触网系统接触力统计量.首先,使用有限元法(FEM)建立并验证受电弓接触网模型,用于生成训练和测试数据集.其次,利用高斯过程回归(GPR)进行对接触力的预测.将一种新开发的元启发式优化,即二元饥饿游戏搜索(HGS),应用于特征选择.为了增强BHGS的性能,嵌入了混沌机制,产生了Chaos-HGS GPR(CHGS-GPR).最后,对CHGS-GPR的预测结果进行了评估.结果发现,所提出的CHGS-GPR对CF的平均值提供r相当准确的预测,并且可以扩展到铁路线路的初步设计、列车运行的实时评估和控制.

Abstract

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 respon-sible 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.

关键词

Pantograph-catenary system/Gaussian process regression/Surrogate model/Physical-based model

Key words

Pantograph-catenary system/Gaussian process regression/Surrogate model/Physical-based model

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基金项目

China National Railway Group Science and Technology Program(N2022T001)

出版年

2024

力学学报(英文版)

CSTPCD

影响因子：0.363

ISSN：0567-7718

参考文献量41

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