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Predicting Storm Outages Through New Representations of Weather and Vegetation | IEEE Journals & Magazine | IEEE Xplore

Predicting Storm Outages Through New Representations of Weather and Vegetation


The University of Connecticut Outage Prediction Model (UConn OPM).

Abstract:

This paper introduces new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their signi...Show More

Abstract:

This paper introduces new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numerical weather prediction outputs, spatially distributed information on soil, vegetation, electric utility assets, and historical power outage data to forecast the number and spatial distribution of outages across the power distribution grid. New modules introduced hereby consist in 1) a storm classifier based on weather variables; 2) a multimodel optimization of regression tree output; and 3) a post-processing routine for more accurately describing tree-leaf conditions. Model implementations are tested through leave-one-storm-out cross-validations performed on 120 storms of varying intensity and characteristics. The results show that the median absolute percentage error of the new OPM version decreased from 130% to 59% for outage predictions at the service territory level, and the OPM skills for operational forecasts are consistent with the skills based on historical storm analyses.
The University of Connecticut Outage Prediction Model (UConn OPM).
Published in: IEEE Access ( Volume: 7)
Page(s): 29639 - 29654
Date of Publication: 01 March 2019
Electronic ISSN: 2169-3536

Funding Agency:


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