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A new predictive risk-based framework is proposed to increase power distribution network resiliency by improving operator understanding of the energy interruption impacts. This paper expresses the risk assessment as the correlation between likelihood and impact. The likelihood is derived from the combination of Naive Bayes learning and Jenks natural breaks classifier. The analytics included in a GIS platform fuse together a massive amount of data from outage recordings and weather historical databases in just one semantic parameter known as failure probability. The financial impact is determined by a time series-based formulation that supports spatiotemporal data from fault management events and customer interruption cost. Results offer prediction of hourly risk levels and monthly accumulated risk for each feeder section of a distribution network allowing for timely risk mitigation.
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This content will become publicly available on August 19, 2027
Network-Level Risks of Extreme Temperature Weather on Rail 1 Infrastructure: 2 A Graph-Based Probability-Impact Risk Assessment Framework
The United States (US) rail network is a large and crucial element of its infrastructure and economy, with approximately 140,000 miles of track. Extreme temperature events can cause rail damage and increase the risk of derailments, putting lives at risk and causing huge economic damage. High rail temperatures during heatwaves can lead to heat-induced buckling, while freezing temperatures can create cracking. Previous research has investigated the effects of temperature on rail failure and the probability of their failure using various approaches such as finite element analysis, lab experiments, machine learning, and others. However, there is a need for research that investigates the higher-level impact of extreme weather events on the large rail network and its operations. Accordingly, this paper addresses that knowledge gap and proposes a framework to investigate the vulnerability of the rail network using a weather-informed graph-based probability-impact risk assessment. A graph of the US rail network is created and embedded with weather data simulating historic extreme weather events; and a probability-impact risk assessment is conducted based on the probability (i.e. what is the probability of a rail segment to fail), impact (i.e. what is the impact if a rail segment fails), and the combined risk severity. The model can quantify the risk severity on all railroads connecting any two nodes, such as any two major cities, on the US rail network. Results of a case study revealed five high-risk regions. This paper proposes a developed framework to support decision making as related to improving the resilience of existing roads and planning new alternative routes to minimize the impact of extreme weather events and improve rail safety.
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- Award ID(s):
- 2112650
- PAR ID:
- 10712666
- Publisher / Repository:
- ASCE
- Date Published:
- Journal Name:
- Journal of infrastructure systems
- ISSN:
- 1943-555X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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