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波动载荷下弓网接触电阻特性及建模研究 被引量:13

Research on Characteristic of the Contact Resistance of Pantograph-Catenary under Load Fluctuation Condition
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摘要 接触电阻是衡量弓网系统受流稳定性、可靠性的重要指标。接触载荷是弓网的调优参数,其波动特性是影响接触电阻的关键因素。基于滑动电接触实验机,开展波动载荷条件下弓网接触电阻的特性研究。首先分析了接触电阻随滑动速度、接触电流、波动频率、波动幅值的变化关系,随后采用ε不敏感支持向量机建立波动载荷、接触电流、滑动速度与接触电阻之间的预测模型,并采用假设检验的方法验证了该模型的有效性。最后,依据所建立的接触电阻模型,在以假定接触电阻上界作为受流约束的算例中,采用可视化的方法分析了速度、电流工况的可行域分布,该方法对弓网的设计与优化具有指导意义。 Contact resistance is an important index to evaluate the stability and reliability of the current-carrying performance of pantograph-catenary system.The contact load is the tuning parameter of the pantograph-catenary system and its fluctuation property is the critical factor affecting contact resistance.Characteristics of contact load are investigated under various load fluctuation conditions based on the sliding electric contact testing machine.Firstly,the changing relation between sliding speed,contact current,fluctuation frequency,fluctuation magnitude and contact resistance is studied.Secondly,the prediction model between fluctuation load,contact current,sliding speed and contact resistance is established usingε-insensitive support vector machine(ε-SVM)and the hypothesis testing method is adopted to verify the effectiveness of the model.Finally,the visualization method is employed to analyze the distribution characteristics of the feasible region of the working conditions of velocity and current in two numerical examples where the assumed upper bound of the contact resistance is taken as the current collection constraint and the method has a guiding significance for design and optimization of pantograph.
作者 时光 陈忠华 郭凤仪 回立川 党伟 Shi Guang;Chen Zhonghua;Guo Fengyi;Hui Lichuan;Dang Wei(Faculty of Electrical and Control Engineering Liaoning Technical University Huludao 125105 China)
出处 《电工技术学报》 EI CSCD 北大核心 2019年第11期2287-2295,共9页 Transactions of China Electrotechnical Society
基金 国家自然科学基金项目(51477071) 辽宁省教育厅科学研究项目(LJ2017QL011,LJ2017QL009) 辽宁工程技术大学生产技术问题创新研究基金(20160068T)资助
关键词 接触电阻 波动载荷 ε不敏感支持向量机 预测模型 假设检验 Contact resistance fluctuation load ε-insensitive support vector machine prediction model hypothesis testing
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