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

IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Wenping Chu, Hui Wang, Yang Song, Zhigang Liu
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引用次数: 0

摘要

在电气化铁路中,受电弓与接触网之间的相互作用对维持稳定的电流供应至关重要。利用有限元方法建立高保真的数值模型是一种普遍需要的方法,但它涉及到相当大的计算复杂度和时间要求。在本文中,我们提出了一种新的动态预测模型,该模型集成了物理信息和数据驱动方法来解决受电弓-接触网相互作用,称为FENet。具体来说,有两个重要的方面:(1)为有效的模拟开发了一个深度学习框架。该网络利用时间卷积网络提取短期局部特征。同时,利用基于注意的长短期记忆来捕捉交互序列中的长期依赖关系。FENet建立了系统状态与激励变量之间的动态关系,实现了快速、准确的仿真。(2)我们整合了多个物理信息损失项来处理运动方程中的隐式约束,从而利用物理原理来指导学习过程。此外,动态加权机制自适应地平衡了物理损失函数中各项的贡献。实验结果表明,FENet对不同的外部激励具有良好的有效性和鲁棒性,计算量可以忽略不计,可以实现长期的动态响应预测。此外,它还显示了在受电弓硬件在环测试平台上进行实时仿真和反馈的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

FENet: A Physics-Informed Dynamics Prediction Model of Pantograph-Catenary Systems in Electric Railway

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.

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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
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
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