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A Multilayer Bayesian Network Approach-Based Predictive Probabilistic Risk Assessment for Overhead Contact Lines Under External Weather Conditions
IEEE Transactions on Transportation Electrification ( IF 7 ) Pub Date : 2022-08-11 , DOI: 10.1109/tte.2022.3198554
Shibin Gao 1 , Jian Wang 1 , Long Yu 1 , Dongkai Zhang 2 , Rui Zhan 1 , Lei Kou 3 , Ke Chen 1
Affiliation  

Due to being completely exposed outdoors without backup, overhead contact lines (OCLs) are more vulnerable to external weather conditions. To perceive the weather-related failure risk and evaluate the associated potential risks, a multilayer Bayesian network (MLBN) approach is proposed for predictive probabilistic risk assessment for OCLs. More specifically, the weather-driven failure probabilities of OCLs are predicted using a first-layer Bayesian network (BN), including lightning strikes, windstorms, and fog-haze. Then, the weather-driven failure patterns of OCLs are investigated and captured, and furthermore, the second-layer BN is embedded to evaluate the risk propagation. Furthermore, the third-layer BN is implemented to calculate and quantify weather-driven risk consequences from perspectives of economic loss and social trust loss constrained by power outage time and train timetable. The three-layer BN models are built by connecting the nodes with causal relationships, avoiding complicated structure learning. The experiments on the actual OCL failure records and weather data demonstrate that the proposed MLBN approach can identify weather-involved risk activities and comprehensively analyze the weather-driven cumulative risks of OCLs from spatiotemporal perspectives. In addition, it is capable of giving reasonable guiding information for mitigating the operational risks of OCLs against external weather conditions.

中文翻译:

基于多层贝叶斯网络方法的外部天气条件下架空接触网预测概率风险评估

由于在没有备份的情况下完全暴露在户外,架空接触网 (OCL) 更容易受到外部天气条件的影响。为了感知与天气相关的故障风险并评估相关的潜在风险,提出了一种多层贝叶斯网络 (MLBN) 方法用于 OCL 的预测概率风险评估。更具体地说,使用第一层贝叶斯网络 (BN) 预测 OCL 的天气驱动故障概率,包括雷击、风暴和雾霾。然后,调查和捕获 OCL 的天气驱动故障模式,此外,嵌入第二层 BN 以评估风险传播。此外,第三层BN实现从停电时间和列车时刻表约束的经济损失和社会信任损失的角度计算和量化天气驱动的风险后果。三层BN模型是通过因果关系连接节点来构建的,避免了复杂的结构学习。对实际 OCL 故障记录和天气数据的实验表明,所提出的 MLBN 方法可以识别与天气有关的风险活动,并从时空角度全面分析 OCL 的天气驱动累积风险。此外,它能够为减轻OCL因外部天气条件而导致的运营风险提供合理的指导信息。三层BN模型是通过因果关系连接节点来构建的,避免了复杂的结构学习。对实际 OCL 故障记录和天气数据的实验表明,所提出的 MLBN 方法可以识别与天气有关的风险活动,并从时空角度全面分析 OCL 的天气驱动累积风险。此外,它能够为减轻OCL因外部天气条件而导致的运营风险提供合理的指导信息。三层BN模型是通过因果关系连接节点来构建的,避免了复杂的结构学习。对实际 OCL 故障记录和天气数据的实验表明,所提出的 MLBN 方法可以识别与天气有关的风险活动,并从时空角度全面分析 OCL 的天气驱动累积风险。此外,它能够为减轻OCL因外部天气条件而导致的运营风险提供合理的指导信息。
更新日期:2022-08-11
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