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随着地铁系统在城市交通中的重要性日益增加，牵引供电系统故障对其运行效率的影响不容忽视。基于复杂网络理论构建了地铁与牵引电网耦合的网络模型，并采用地铁站点间流量加权网络效率指标分析牵引电网故障对地铁网络效率的影响。通过设定多种故障场景，包括供电区间失效和供电节点故障，采用蒙特卡洛方法进行独立随机故障、聚簇故障模拟，评估不同故障情境下地铁网络的性能波动。结果表明：关键供电区间与节点的失效显著降低了地铁网络的运行效率，且高峰时段效应明显放大；当多处供电节点与区间联动失效时，地铁网络运行效率剧烈下降，体现了地铁网络在复杂故障场景下的脆弱性。研究结果为供电关键区段和节点识别、设备运维优先级排序及高峰期运行组织优化提供了实证支撑，促进地铁网络应对供电系统故障的韧性提升。
Abstract：A coupled model of the metro operating network and the traction power supply network is developed to analyze the degradation of metro operating efficiency resulting from power supply failures. The metro network is represented as a network of metro stations and edges， while the power supply network is modeled as a network of power supply nodes and sections. Based on the mapping between power supply sections and metro edges， a cross-network fault propagation mechanism is established. The relationship between the capacity degradation of power supply nodes and the failure of power supply sections is characterized using supply-demand theory. When the available traction power becomes insufficient， train speeds on the affected edges are calculated using a power-speed mapping function， and the corresponding travel times are updated to reflect travel costs under fault conditions. The Stochastic User Equilibrium (SUE) assignment method simulates passenger route choices， taking into account travel time and other related factors. Passenger-flow-weighted network efficiency is employed as the performance indicator for the system; unreachable Origin-Destination (OD) flows are treated as lost demand and excluded from the network efficiency calculation under fault conditions. Three classes of failure scenarios are designed based on the characteristics of traction power supply faults: successive power supply section failures， successive power supply node failures， and independent random failures generated through Monte Carlo simulation. A local metro network in a specific city is selected as a case study， applying the model using smart-card OD data. Experiments under the successive power supply section failure scenario identify critical sections that significantly impact network efficiency， with efficiency losses exceeding 30% during peak hours. Similarly， experiments under the successive power supply node failure scenario identify critical nodes， which induce an average daily efficiency loss of approximately 27%. Both failure scenarios exhibit pronounced temporal characteristics， indicating that the metro network is significantly more sensitive to power supply failures during peak hours than during off-peak hours. Monte Carlo analyses further reveal the system's high vulnerability to spatially clustered failures and independent random failures. Independent random failures can reduce relative metro network efficiency by more than 80% during peak hours， while spatially clustered failures yield a higher mean relative efficiency but involve a substantial risk of abrupt efficiency collapse. The proposed method facilitates the identification and ranking of critical power supply units and quantifies the fluctuations in metro network efficiency under various failure scenarios and demand levels， providing theoretical support for maintenance prioritization and operational planning during peak hours.
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基本信息:
DOI：10.13637/j.issn.1009-6094.2025.2185
中图分类号:U231.8
引用信息:
[1]李洁,赵鑫阳,罗瑛,等.考虑牵引供电系统故障的地铁网络效率分析[J].安全与环境学报().DOI:10.13637/j.issn.1009-6094.2025.2185.
基金信息:
国家自然科学青年基金项目（52407095）; 国网总部项目（521001250025–073–ZN）; 湖南省交通运输厅科技项目（202322）; 贵州省交通运输厅科技计划项目（2025–112–018）
2026-08-27
2026-08-27
2026-08-27