Predicting electricity distribution feeder failures using machine learning susceptibility analysis

  • Authors:

  • Philip Gross;Albert Boulanger;Marta Arias;David Waltz;Philip M. Long;Charles Lawson;Roger Anderson;Matthew Koenig;Mark Mastrocinque;William Fairechio;John A. Johnson;Serena Lee;Frank Doherty;Arthur Kressner

  • Affiliations:

  • Columbia University, Center for Computational Learning Systems, New York, NY;Columbia University, Center for Computational Learning Systems, New York, NY;Columbia University, Center for Computational Learning Systems, New York, NY;Columbia University, Center for Computational Learning Systems, New York, NY;Google Inc, Mountain View, CA and Columbia University, Center for Computational Learning Systems, New York, NY;Digimedia Corp, Austerlitz, NY and Columbia University, Center for Computational Learning Systems, New York, NY;Columbia University, Center for Computational Learning Systems, New York, NY;Consolidated Edison Company of New York, New York, NY;Consolidated Edison Company of New York, New York, NY;Consolidated Edison Company of New York, New York, NY;Consolidated Edison Company of New York, New York, NY;Consolidated Edison Company of New York, New York, NY;Consolidated Edison Company of New York, New York, NY;Consolidated Edison Company of New York, New York, NY

  • Venue:
  • IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
  • Year:
  • 2006

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Abstract

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 York City. In Manhattan, rankings are updated every 20 minutes and displayed on distribution system operators' screens. Additionally, a separate system makes seasonal predictions of failure susceptibility. These feeder failures, known as "Open Autos" or "O/As," are a significant maintenance problem. A year's sustained research has led to a system that demonstrates high accuracy: 75% of the feeders that actually failed over the summer of 2005 were in the 25% of feeders ranked as most at-risk. By the end of the summer, the 100 most susceptible feeders as ranked by the ML system were accounting for up to 40% of all O/As that subsequently occurred each day. The system's algorithm also identifies the factors underlying failures which change over time and with varying conditions (especially temperature), providing insights into the operating properties and failure causes in the feeder system.