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

doi:10.1007/s10409-023-23282-x

IMECH-IR > 流固耦合系统力学重点实验室

	A feasibility study on applying meta-heuristic optimization and Gaussian process regression for predicting the performance of pantograph-catenary system
	Zhang MH(张莫晗); Yin B(银波); Sun ZX(孙振旭); Bai, Ye; Yang GW(杨国伟)
通讯作者	Yin, Bo(yinbo@imech.ac.cn)
发表期刊	ACTA MECHANICA SINICA
	2024
卷号	40 期号:1 页码:12
ISSN	0567-7718
摘要	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.
关键词	Pantograph-catenary system Gaussian process regression Surrogate model Physical-based model
DOI	10.1007/s10409-023-23282-x
收录类别	SCI ; EI ; CSCD
语种	英语
WOS记录号	WOS:001150527300004
关键词[WOS]	VEHICLE SUSPENSION
WOS研究方向	Engineering ; Mechanics
WOS类目	Engineering, Mechanical ; Mechanics
资助项目	China National Railway Group Science and Technology Program[N2022T001]
项目资助者	China National Railway Group Science and Technology Program
论文分区	二类
力学所作者排名	1
RpAuthor	Yin, Bo
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/94265
专题	流固耦合系统力学重点实验室
推荐引用方式 GB/T 7714	Zhang MH,Yin B,Sun ZX,et al. A feasibility study on applying meta-heuristic optimization and Gaussian process regression for predicting the performance of pantograph-catenary system[J]. ACTA MECHANICA SINICA,2024,40,1,:12.
APA	张莫晗,银波,孙振旭,Bai, Ye,&杨国伟.(2024).A feasibility study on applying meta-heuristic optimization and Gaussian process regression for predicting the performance of pantograph-catenary system.ACTA MECHANICA SINICA,40(1),12.
MLA	张莫晗,et al."A feasibility study on applying meta-heuristic optimization and Gaussian process regression for predicting the performance of pantograph-catenary system".ACTA MECHANICA SINICA 40.1(2024):12.