Columbia University

Consolidated Edison of New York, Inc.

Phil Long

Roger N Anderson

Albert Boulanger

John A Johnson

Arthur Kressner

Marta Arias

Philip Gross

Mark Mastrocinque

Hila Becker

David Waltz

Matthew Koenig

A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.

System and method for grading electricity distribution network feeders susceptible to impending failure

Growing community of inventors

Palo Alto, CA, United States of America

Phil Long