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

US7945524B2 · kind B2 · utility

Assignees

• The Trustees of Columbia University in the City of New York · US
• CONSOLIDATED EDISON COMPANY OF NEW YORK, INC. · US

Inventors

• Roger N. Anderson · New York, US
• Albert Boulanger · New York, US
• David Waltz · Princeton, US
• Phil Long · Palo Alto, US
• Marta Arias · Barcelona, ES
• Philip S. Gross · Meriden, US
• Hila Becker · Plainview, US
• Arthur Kressner · Westfield, US
• Mark Mastrocinque · East Northport, US
• Matthew W. Koenig · Dacono, US
• John Andrew Johnson · Delafield, US

Key dates

• Filing date: Jul 23, 2008
• Grant date: May 17, 2011
• Expiry date: Jan 10, 2029

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/00
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

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.