Research on Simulation and Energy Saving Optimization of Regenerative Braking System for Subway

被引：4
|
作者：
Zhang C. [1 ]
Tan N. [1 ]
Liu M. [2 ]
Su S. [1 ]
Xu W. [1 ]
机构：
[1] School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing
[2] Equipment Management Department, Changzhou Rail Transit Development Co., Ltd., Changzhou, 213022, Jiangsu
来源：
Zhongguo Tiedao Kexue/China Railway Science | 2019年 / 40卷 / 03期
关键词：
Gravitational search algorithm; Inverter feedback; Regenerative braking; Subway train; Traction substation; Train working diagram;
D O I：
10.3969/j.issn.1001-4632.2019.03.16
中图分类号：
学科分类号：
摘要：
The model of the inverter-feedback type traction power supply system was established by simplifying a train into a controlled current source. By using Simulink software, a full-line system model of the subway train in series was built, the voltage variation curve of each traction substation as well as the energy via the regenerative braking inverter feedback was calculated. With the optimization target of minimizing the power loss of the equivalent resistance of power supply arm and the running rail when full line subway trains running in opposite directions, the setting strategy of traction substations was optimized. In order to prolong the total overlap time of the traction and braking of adjacent trains, train working diagram was optimized by gravitational search algorithm. Taking a subway under construction as an example, in order to reduce the energy consumption of power grid and improve the utilization of regenerative energy, the optimized setting of traction substations and the optimized adjustment of train working diagram were studied by simulation. It is found that the locations of traction substations should be set as evenly as possible. By using the gravitational search algorithm to optimize train working diagram, the overlap time of the traction and braking of adjacent trains can be maximized and the energy saving efficiency can be improved at the same time. © 2019, Editorial Department of China Railway Science. All right reserved.
引用
收藏
页码：112 / 118
页数：6
相关论文
共 16 条
  • [1] Peng S., Zhu X., Qin B., Et al., The Simulation Research of the Subway Regenerative Braking Energy Absorption System Based on SVPWM Control, New Industrialization Strategy, 5, 3, pp. 20-27, (2015)
  • [2] Xun J., Tang T., Song X., Et al., Comprehensive Model for Energy-Saving Train Operation of Urban Mass Transit under Regenerative Brake, China Railway Science, 36, 1, pp. 104-110, (2015)
  • [3] Huo C., Inverter Type of Regenerative Braking Energy Utilization Device of Urban Rail Transit Capacity Analysis and Calculation, Techniques of Automation and Applications, 35, 9, pp. 75-78, (2016)
  • [4] Wang Q., Feng X., Zhu J., Et al., Simulation Study on Optimal Energy-Efficient Control of High Speed Train Considering Regenerative Brake Energy, China Railway Science, 36, 1, pp. 96-103, (2015)
  • [5] Liu J., Guo H., Yu Y., Research on the Cooperative Train Control Strategy to Reduce Energy Consumption, IEEE Transactions on Intelligent Transportation Systems, 18, 5, pp. 1134-1142, (2017)
  • [6] Sun X., Lu H., Dong H., Energy-Efficient Train Control by Multi-Train Dynamic Cooperation, IEEE Transactions on Intelligent Transportation Systems, 18, 11, pp. 3114-3121, (2017)
  • [7] Wang B., Hu H., Gao S., Et al., Power Flow Calculation and Analysis for High-Speed Railway Traction Network under Regenerative Braking, China Railway Science, 35, 1, pp. 86-93, (2014)
  • [8] Gupta S.D., Pavel L., Kevin T.J., An Optimization Model to Utilize Regenerative Braking Energy in a Railway Network, 2015 American Control Coference(ACC), pp. 5919-5924, (2015)
  • [9] Torre S.D.L., SAnchez-Racero A.J., Aguado J.A., Et al., Optimal Sizing of Energy Storage for Regenerative Braking in Electric Railway Systems, IEEE Transactions on Power Systems, 30, 3, pp. 1492-1500, (2015)
  • [10] Yin J., Chen D., Zhao W., Et al., Online Adjusting Subway Timetable by Q-Learning to Save Energy Consumption in Uncertain Passenger Demand, IEEE, International Conference on Intelligent Transportation Systems, pp. 2743-2748, (2014)
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