Predicting electricity distribution feeder failures using machine learning susceptibility analysis

Abstract

This work has been partly supported by a research contract from Consolidated Edison. A Machine Learning (ML) System known as ROAMS (Ranker for Open-Auto Maintenance Scheduling) was developed to create failure-susceptibility rankings for almost one thousand 13.8kV-27kV energy distribution feeder cables that supply electricity to the boroughs of New