考虑双向变流器的城市轨道交通列车节能运行一体化方法
荀径1李艳艳1王忻2谢立军3曹希雯1
Integrated Approach for Energyefficient Considering Bidirectional Converters in Urban Rail Transit
XUN JingLI YanyanWANG XinXIE LijunCAO Xiwen
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作者信息:1.北京交通大学电子信息工程学院， 100044， 北京
2.中国铁道科学研究院集团有限公司城市轨道交通中心， 100081， 北京
3.中车青岛四方机车车辆股份有限公司， 266111， 青岛
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Affiliation:School of Electronic and Information Engineering, Beijing Jiaotong University, 100044, Beijing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.10.020
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中图分类号/CLCN:TK01+8∶U284.48
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栏目/Col:应用技术
摘要:
目的：鉴于目前我国城轨节能压力巨大，节能一体化优化研究受到广泛关注，而双向变流技术作为提高再生能利用率、降低净能耗的手段之一，因此有必要开展考虑双向变流器的城市轨道交通列车节能运行一体化方法的研究。方法：以整条城轨线路作为优化对象，建立了考虑双向变流器的列车运行图与速度曲线一体化模型。针对此大规模优化问题求解效率低的问题，提出一种QPSO(Q学习PSO(粒子群))算法。以北京地铁燕房线上的列车实际运行数据为例，对考虑双向变流器的城市轨道交通列车节能运行一体化方法的有效性进行了仿真验证。结果及结论：相较PSO算法，QPSO算法具有更好的全局搜索特性；相较未考虑双向变流器的模型，考虑双向变流器的城市轨道交通列车节能运行一体化方法可以将再生能利用率由31.2%提高至74.8%，并降低净能耗26.9%。
Abstracts:
Objective: Given the substantial energysaving pressure faced by URT (urban rail transit) in China, research on energysaving integration receives extensive attention. BC (bidirectional converter) technology is considered one of the means to increase the utilization of regenerative energy and reduce net energy consumption. Therefore, it is essential to conduct research on an integrated energysaving method for URT trains considering BC. Method: The entire URT line is taken as the optimization object, the integrated model of train timetable and speed curve considering BC is established. To address the efficiency issues in solving this largescale optimization problem, a QPSO (Qlearning particle swarm optimization) algorithm is proposed. Using actual train operation data from the Yanfang Line of Beijing Subway as example, the effectiveness of the integrated energysaving method for URT trains considering BC is validated through simulations. Result & Conclusion: Compared to PSO algorithm, the QPSO algorithm exhibits superior global search capabilities. Compared to models without considering BC, the integrated energysaving method for URT trains considering BC can increase the regenerative energy utilization rate from 31.2% to 74.8% and reduce net energy consumption by 26.9%.
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