conEd

conEd

Current and recent Con Edison projects
illustrate how the Center is applying machine learning to crucial
challenges confronted by the public utility providing electricity and
gas to the New York City metropolitan area.

Con Edison’s Challenge: To
ensure compliance with regulatory requirements and for legal reasons,
Con Edison had saved over twenty years of records. New York City was
one of the earliest major cities to be electrified over 180 years ago.
Unlike more modern systems in cities like Tokyo and Las Vegas, most of
the 30,000 plus cable sections of paper insulated lead sheathed cable,
each a block long, are aging. Research Scientists at the Center are
using machine learning capabilities to understand patterns (including
400 mgs of data about feeder failures daily), then predict which
components are most susceptible to feeder failure and which are most
likely to work well, based on age, location, weather and other
conditions as well as historical events.

The Need to Understand Patterns of Failure: Because
the maintenance and repairs of each of the 30,000+ cable section
involves a series of tasks from pumping water and vermin out of
manholes, shutting down power safely, pulling out old cable out of 3-16
splices at each end of the section and replacing them with new cable.

The Value of the Outcome: The
work is increasing predictability of when and where feeders in the
distribution system are susceptible to future failure. In 2005, CCLS
algorithms, incorporating multiple attributes with the MartiRank
method, predicted 40 percent of future outages for the data they had
reviewed. As the computational systems “learn” more about the data,
they reconfigure themselves. As the system generates models, CCLS
research scientists refine rules, with the goal of moving towards 80
percent accuracy. This predictability gives Con Edison information
needed to schedule maintenance in ways that will produce a geometric
improvement in continuous power service to millions of customers and a
reduction of costly emergency service.

Current and recent Con Edison Projects:

Columbia Learning System for Prioritization of Feeders for Long-Term Replacement Program for Cable and Splice Center of Excellence. Contract with Consolidated Edison.
Senior Team Members: Roger Anderson, David Waltz, Albert Boulanger, Phil Long

Columbia Decision Support System for the Manhattan Electric Control Center (MECC) 2005
Contract with Consolidated Edison.
Senior Team Members: Roger Anderson, David Waltz, Albert Boulanger, Phil Long

For more information, see article by Columbia’s Earth Institute, available online at http://www.earthinstitute.columbia.edu/news/2005/story06-01-05e.html