基于免疫退火遗传算法的城市轨道交通列车节能运行策略
张明锐李俊江林永乐韦莉
Research on Energy-saving Operation Strategy of Urban Rail Transit Train Based on Immune Annealing Genetic Algorithm
ZHANG MingruiLI JunjiangLIN YongleWEI Li
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作者信息:同济大学电子与信息工程学院,201804,上海
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Affiliation:College of Electronics and Information Engineering, Tongji University, 201804, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2021.12.006
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中图分类号/CLCN:U292.4;TK01+8
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栏目/Col:研究报告
摘要:
为降低城市轨道交通列车站间运行能耗，提出了一种基于免疫退火遗传算法(IAGA)的列车节能运行策略。在区间运行时间固定的情况下，该策略能实现列车能耗的最小化。IAGA算法通过免疫补充和建立疫苗库缩短算法收敛时间，并借助退火机制避免算法提前进入早熟状态。求解模型以寻找列车站间运行工况转换点为约束，牵引能耗最小为目标，建立列车节能运行策略。以实际线路数据和车辆参数为基础，对单区间及三站两区间的节能策略进行仿真验证。结果表明，在保证站间列车定时运行的情况下，与传统遗传算法相比，IAGA算法具有更快的收敛速度，其计算的策略可使单区间列车运行能耗降低10.2%，三站两区间列车运行总能耗降低16.0%。
Abstracts:
In order to reduce the energy consumption of urban rail transit trains running at intervals, a train energy-saving operation strategy based on immune annealing genetic algorithm (IAGA) is proposed. The strategy minimizes the energy consumption of the train when the interval running time is fixed. The IAGA shortens the convergence time of the algorithm by immune supplementation and establishing a vaccine library. By means of the annealing mechanism, the algorithm is prevented from falling into prematurity. The model is solved to find train interval operating condition switching point as the constraint, taking minimal traction energy consumption as the goal, and train energy\|saving operation strategy is established. Based on actual line data and vehicle parameters, simulation of single interval and two intervals between three stations is conducted for energy\|saving strategy verification. The results show that on the premises of ensuring fixed interval running time, compared to conventional genetic algorithm, the IAGA has a faster convergence, and the calculated strategy can lower train energy consumption at single interval by 10.2%, and that at two intervals between three stations by 16.0%.
引文 / Ref:
张明锐，李俊江，林永乐，等.基于免疫退火遗传算法的城市轨道交通列车节能运行策略[J].城市轨道交通研究，2021，24(12):28.
ZHANG Mingrui，LI Junjiang，LIN Yongle，et al.Research on energy-saving operation strategy of urban rail transit train based on immune annealing genetic algorithm[J].Urban Mass Transit，2021，24(12)：28.
ZHANG Mingrui，LI Junjiang，LIN Yongle，et al.Research on energy-saving operation strategy of urban rail transit train based on immune annealing genetic algorithm[J].Urban Mass Transit，2021，24(12)：28.
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