A Multilayer Bayesian Network Approach-Based Predictive Probabilistic Risk Assessment for Overhead Contact Lines Under External Weather Conditions

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.

I. Introduction

Overhead contact lines (OCLs), as electricity bridges for transmitting power from traction power supply systems to electric locomotives [1], [2], [3], are playing an increasingly prominent role in energy conversion and supply. However, due to being completely exposed outdoors without backup, the OCLs are more vulnerable to external weather conditions, including windstorms, lighting strikes, for-haze, cold waves, and heavy rain, as depicted in Fig. 1. They are likely to cause various weather-related failures with spatial and temporal differences, owning to a wide span of OCLs and seasonal variations in severe weather conditions. Meanwhile, the statistical analysis of historic failure records shows that the external weather conditions have become the main causes of OCL failures [4], which may lead to interruption of service, serious economic losses (ELs), unfavorable social influence, and so on [5]. Therefore, to identify the weather-involved risk activities and mitigate the operational risks caused by external weather conditions, it is significantly dispensable to comprehensively evaluate the dynamic weather-driven risks, incorporating spatiotemporal impacts of continuous weather changes and the corresponding risk consequences. Fig. 1.

Structure and external failure types of OCLs.