Machine learning for power grid

US8751421B2 · 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
• Cynthia Rudin · New York, US
• David Waltz · Princeton, US
• Ansaf Salleb-Aouissi · Middle Village, US
• Maggie Chow · Hartsdale, US
• Haimonti Dutta · Trenton, US
• Phil Gross · Brooklyn, US
• Huang Bert · Silver Spring, US
• Steve Ierome · New York, US
• Delfina Isaac · New York, US
• Arthur Kressner · Westfield, US
• Rebecca J. Passonneau · New York, US
• Axinia Radeva · New York, US
• Leon L. Wu · New York, US
• Peter Hofmann · Hasbrouck Heights, US
• Frank Dougherty · Yorktown Heights, US

Key dates

• Filing date: Jan 15, 2013
• Grant date: Jun 10, 2014
• Expiry date: Jan 15, 2033

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y04S10/52
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.