P. Gross,
A. Boulanger,
M. Arias, et al.
Predicting electricity
distribution feeder failures using machine learning
susceptibility analysis. IAAI'06.
(Available
in PDF format.)
Abstract
A machine learning (ML) system known as ROAMS (Ranker for
Open-Auto Maintenance Scheduling) was developed to create
failure-susceptibility rankings for almost 1000 13.8kV-27kV
energy distribution feeders 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. A separate system makes seasonal predictions
of failure susceptibility. These feeder failures, known as "Open Autos",
or "O/As", are a significant maintanence 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), proving insights into the operating principles and
failure causes in the feeder system.