{"title":"电气化铁路受电弓接触网系统的物理信息动力学预测模型","authors":"Wenping Chu, Hui Wang, Yang Song, Zhigang Liu","doi":"10.1049/itr2.70059","DOIUrl":null,"url":null,"abstract":"<p>In electric railways, the interaction performance between the pantograph and catenary is crucial for maintaining a stable current supply. Establishing high-fidelity numerical models using the finite element method is generally desirable, yet it involves considerable computational complexity and time demands. In this paper, we propose a novel dynamic prediction model that integrates physical information and data-driven approaches to solve the pantograph-catenary interaction, called FENet. Specifically, there are two significant aspects: (1) A deep learning framework is developed for efficient simulation. The network utilises the temporal convolutional network to extract short-term local features. Simultaneously, the attention-based long short-term memory is leveraged to capture the long-term dependencies in the interaction sequence. FENet establishes the dynamic relationship between the system state and excitation variables, achieving fast and accurate simulation. (2) We integrate multiple physics-informed loss terms to handle implicit constraints within motion equations, which leverages physical principles to guide the learning process. Additionally, a dynamic weighting mechanism adaptively balances the contributions of various terms in the physics-based loss function. Experimental results reveal that FENet exhibits effectiveness and robustness against different external excitations and achieves long-term dynamic response prediction with negligible computational effort. Moreover, it shows promising potential for real-time simulation and feedback in pantograph hardware-in-the-loop test rigs.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70059","citationCount":"0","resultStr":"{\"title\":\"FENet: A Physics-Informed Dynamics Prediction Model of Pantograph-Catenary Systems in Electric Railway\",\"authors\":\"Wenping Chu, Hui Wang, Yang Song, Zhigang Liu\",\"doi\":\"10.1049/itr2.70059\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In electric railways, the interaction performance between the pantograph and catenary is crucial for maintaining a stable current supply. Establishing high-fidelity numerical models using the finite element method is generally desirable, yet it involves considerable computational complexity and time demands. In this paper, we propose a novel dynamic prediction model that integrates physical information and data-driven approaches to solve the pantograph-catenary interaction, called FENet. Specifically, there are two significant aspects: (1) A deep learning framework is developed for efficient simulation. The network utilises the temporal convolutional network to extract short-term local features. Simultaneously, the attention-based long short-term memory is leveraged to capture the long-term dependencies in the interaction sequence. FENet establishes the dynamic relationship between the system state and excitation variables, achieving fast and accurate simulation. (2) We integrate multiple physics-informed loss terms to handle implicit constraints within motion equations, which leverages physical principles to guide the learning process. Additionally, a dynamic weighting mechanism adaptively balances the contributions of various terms in the physics-based loss function. Experimental results reveal that FENet exhibits effectiveness and robustness against different external excitations and achieves long-term dynamic response prediction with negligible computational effort. Moreover, it shows promising potential for real-time simulation and feedback in pantograph hardware-in-the-loop test rigs.</p>\",\"PeriodicalId\":50381,\"journal\":{\"name\":\"IET Intelligent Transport Systems\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70059\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Intelligent Transport Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/itr2.70059\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Intelligent Transport Systems","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/itr2.70059","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
FENet: A Physics-Informed Dynamics Prediction Model of Pantograph-Catenary Systems in Electric Railway
In electric railways, the interaction performance between the pantograph and catenary is crucial for maintaining a stable current supply. Establishing high-fidelity numerical models using the finite element method is generally desirable, yet it involves considerable computational complexity and time demands. In this paper, we propose a novel dynamic prediction model that integrates physical information and data-driven approaches to solve the pantograph-catenary interaction, called FENet. Specifically, there are two significant aspects: (1) A deep learning framework is developed for efficient simulation. The network utilises the temporal convolutional network to extract short-term local features. Simultaneously, the attention-based long short-term memory is leveraged to capture the long-term dependencies in the interaction sequence. FENet establishes the dynamic relationship between the system state and excitation variables, achieving fast and accurate simulation. (2) We integrate multiple physics-informed loss terms to handle implicit constraints within motion equations, which leverages physical principles to guide the learning process. Additionally, a dynamic weighting mechanism adaptively balances the contributions of various terms in the physics-based loss function. Experimental results reveal that FENet exhibits effectiveness and robustness against different external excitations and achieves long-term dynamic response prediction with negligible computational effort. Moreover, it shows promising potential for real-time simulation and feedback in pantograph hardware-in-the-loop test rigs.
期刊介绍:
IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following:
Sustainable traffic solutions
Deployments with enabling technologies
Pervasive monitoring
Applications; demonstrations and evaluation
Economic and behavioural analyses of ITS services and scenario
Data Integration and analytics
Information collection and processing; image processing applications in ITS
ITS aspects of electric vehicles
Autonomous vehicles; connected vehicle systems;
In-vehicle ITS, safety and vulnerable road user aspects
Mobility as a service systems
Traffic management and control
Public transport systems technologies
Fleet and public transport logistics
Emergency and incident management
Demand management and electronic payment systems
Traffic related air pollution management
Policy and institutional issues
Interoperability, standards and architectures
Funding scenarios
Enforcement
Human machine interaction
Education, training and outreach
Current Special Issue Call for papers:
Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf
Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf
Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf