Storm outage modeling for an electric distribution network in Northeastern USA

D. Wanik, E. Anagnostou (), B. Hartman, M. Frediani and M. Astitha

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2015, vol. 79, issue 2, 1359-1384

Abstract: The interaction of severe weather, overhead lines and surrounding trees is the leading cause of outages to electric distribution networks in forested areas. In this paper, we show how utility-specific infrastructure and land cover data, aggregated around overhead lines, can improve outage predictions for Eversource Energy (formerly Connecticut Light and Power), the largest electric utility in Connecticut. Eighty-nine storms from different seasons (cold weather, warm weather, transition months) in the period 2005–2014, representing varying types (thunderstorms, blizzards, nor’easters, hurricanes) and outage severity, were simulated using the Weather Research and Forecasting (WRF) atmospheric model. WRF simulations were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This framework could be used for predicting outages to other types of critical infrastructure networks with benefits for emergency-preparedness functions in terms of equipment staging and resource allocation. Copyright Springer Science+Business Media Dordrecht 2015

Keywords: Electric distribution network; Outage; Critical infrastructure; Machine learning; Numerical weather prediction; Land cover; Hurricanes (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (16)

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DOI: 10.1007/s11069-015-1908-2

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