研究目的: 针对高速铁路接触网系统四种维修方式,本文以接触网系统可靠性最高和维修费用最低为目标,建立基于维修方式组合的预防维修系统多目标优化模型,提出一种改进多目标粒子群优化算法对该模型进行求解。首先采用PWLMC混沌映射生成初始化种群,增强种群多样性,然后采取非线性递减的惯性权重调整策略,提高算法寻优精度和收敛速度。在种群进化过程中,对每一代粒子使用归一化越界处理方法,防止算法陷入早熟。
研究结论: (1)提出的改进多目标粒子群优化算法与现有算法相比,Pareto最优解前端的分布范围更广且种群多样性及获得的最优解精度均有提高;(2)在维修次数取13的情况下,改进算法获得的维修方案及该方案下各部件的动态可靠性能均达到较理想效果;(3)改进多目标粒子群优化算法可在满足实际安全运行的要求下得到最优决策方案,有效减低接触网系统维修费用的同时提高其可靠性,为高速铁路接触网系统可靠性评估与维修计划决策提供有效方法。
Abstract
Research purposes: Aiming at the four maintenance methods of contact network system of high-speed railroad, this paper establishes a multi-objective optimization model of preventive maintenance system based on the combination of maintenance methods with the goal of the highest reliability and the lowest maintenance cost of the contact network system, and proposes a kind of improved multi-objective particle swarm optimization algorithm to solve the model. Firstly, PWLMC chaotic mapping is used to generate the initialized population to enhance the population diversity, and then a nonlinear decreasing inertia weight adjustment strategy is adopted to improve the algorithm's optimization accuracy and convergence speed. In the process of population evolution, the normalized transgression processing method is used for each generation of particles to prevent the algorithm from falling into premature maturity.
Research conclusions: (1)Compared with the existing algorithm, the proposed improved multi-objective particle swarm optimization algorithm has a wider distribution range of the Pareto optimal solution front end, and the population diversity and the obtained optimal solution accuracy are all improved;(2)Under the condition that the number of maintenance is 13, the maintenance scheme obtained by the improved algorithm and the dynamic reliability performance of each component under the scheme have achieved ideal results;(3)The improved multi-objective particle swarm optimization algorithm can obtain a reasonable decision-making scheme under the requirement of actual safe operation, which can effectively reduce the maintenance cost of the system and improve its reliability, and provide an effective method for theperformance evaluation and maintenance planning decision.
关键词
接触网系统 /
电气化铁路 /
改进的多目标粒子群优化算法 /
维修费用 /
可靠度 /
归一化
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Key words
contact network system /
electrified railway /
improved multi-target particle group optimization algorithm /
maintenance cost /
reliability /
normalization
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中图分类号：
TP273
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参考文献
[1] 张家玮,杨佳,苏凯新,等.吊弦非均布下400 km/h接触网动态性能提升研究[J].铁道工程学报,2023(7):73-79.
Zhang Jiawei, Yang Jia, Su Kaixin, etc. Research on the Dynamic Performance Improvement of 400 km/h Catenary under Non-uniform Distribution of Droppers[J]. Journal of Railway Engineering Society, 2023(7): 73-79.
[2] 何勇,王红,杨国军,等.基于可靠度的牵引接触网系统分阶段成组维修策略[J].兰州交通大学学报,2020(3):84-88.
He Yong, Wang Hong, Yang Guojun, etc. Multi-period Grouping Maintenance Strategies of Traction Catenary System Based on Reliability[J]. Journal of Lanzhou Jiaotong University,2020(3):84-88.
[3] 关金发,田志军,张学武.接触网弹性吊索参数对弓网动态性能影响[J].西南交通大学学报,2021(3):659-665.
Guan Jinfan, Tian Zhijun, Zhang Xuewu. Influence of Catenary Stitch Wire Parameters on Dynamic Performance Between Pantograph and Catenary[J]. Journal of Southwest Jiaotong University, 2021(3): 659-665.
[4] 刘葛辉,陈绍宽,刘爽,等.资源约束下城市轨道交通基础设施维修任务安排优化模型[J].交通运输系统工程与信息,2021(3):163-169.
Liu Gehui, Chen Shaokuan, Liu Shuang, etc. Mainenance Arrangement Optimization for Infrastructure Systems of Urban Rail Transit Considering Resource Constraints[J]. Journal of Transportation Systems Engineering and Information Technology,2021(3):163-169.
[5] 李雪,吴俊勇,杨媛,等.高速铁路接触网悬挂系统维修计划的优化研究[J].铁道学报,2010(2):24-30.
Li Xue,Wu Junyong,Yang Yuan, etc. Research on Optimization of Catenary System Maintenance Schedule for High-speed Railways[J].Journal of the China Railway Society,2010(2):24-30.
[6] 段晓晨,钱睿,孟楠.隧道工程运维成本PSO、BPNN、BIM控制方法研究[J].铁道工程学报,2021(7):100-105.
Duan Xiaochen, Qian Rui, Meng Nan. Research on the Control Methods ofPSO,BPNN,and BIM for the Operation and Maintenance Cost of Tunnel Engineering[J]. Journal of Railway Engineering Society, 2021(7): 100-105.
[7] 禚真福,杨永建,樊晓光.阵列方向图综合中PSO算法粒子越界处理研究[J].现代防御技术, 2016(1):218-224.
Zhuo Zhenfu, Yang Yongjian, Fan Xiaoguang. Disposal of Particles Cross Border of PSO Algorithm in Beam Pattern Synthesis[J].Modern Defense Technology,2016(1):218-224.
[8] 陈绍宽.铁路牵引供电系统维修计划优化模型与算法[D].北京:北京交通大学,2006.
Chen Shaokuan. Optimization Model and Algorithm for Maintenance Planning of Railway Traction Power Supply System[D]. Beijing: Beijing Jiaotong University, 2006.
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脚注
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基金
国家自然科学基金地区科学基金项目(51867009);江西省自然科学基金项目(20181BAB202017);江西省教育厅科学技术研究青年 项目(GJJ190354,GJJ190295);江西省创新创业大学生训练计划省级重点项目(202110404085);江西省高层次高技能领军人才计划 项目
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