Columbia University

Consolidated Edison Company of New York, Inc.

Other

Ulc Technologies, LLC

Consolidated Edison Energy Company of New York

Maggie Chow

Roger N Anderson

Albert Boulanger

Leon L Wu

Arthur Kressner

Aalap Rajendra Shah

Alex Symington

Mark Mastrocinque

Philip Gross

David Waltz

Dennis John Semet

Caner Oktayer

George Murray

Luis Jerez

Cynthia Rudin

Ansaf Salleb-Aouissi

Thomas Campbell

Haimonti Dutta

Phil Gross

Michael Baratta

Huang Bert

Than Lin Htaik

Steve Ierome

Delfina Isaac

Rebecca J Passonneau

Axinia Radeva

Patrick McHugh

Peter Hofmann

Frank Dougherty

Robert J Blick

A system for servicing cable includes a field-end assembly and a live-end assembly, each mounted on a support structure having at least one drive mechanism operable to cause relative linear movement between the field-end assembly and the live-end assembly. A drilling-and-shorting assembly can create a short circuit in a field-end of the cable, and a continuity tester can test the integrity of the short-circuit. The field end of the cable can then be ejected from the system, and an end-cap-cradle assembly can position an end cap on a live end of an electrical cable and test its installation.

System for servicing cable

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.

Machine learning for power grid

A capital asset planning system for selecting assets for improvement within an infrastructure that includes one or more data sources descriptive of the infrastructure, one or more databases, coupled to the one or more data sources, to compile the one or more data sources, one or more processors, each coupled to and having respective communication interfaces to receive data from the one or more databases. The processor includes a predictor to generate a first metric of estimated infrastructure effectiveness based, at least in part, on a current status of the infrastructure, a second metric of estimated infrastructure effectiveness based, at least in part, on a user-selected, proposed changed configuration of the infrastructure, and a net metric of infrastructure effectiveness based, at least in part, on said first metric and said second metric. The system also includes a display, coupled to have the one or more processors, for visually presenting the net metric of infrastructure effectiveness, in which the assets for improvement are selected based, at least in part, on the net metric of infrastructure effectiveness.

Capital asset planning system

A system and computer-implemented method of providing contingency analysis information for a utility service network that includes obtaining contingency analysis information from a plurality of external sources, integrally combining the contingency analysis information obtained from each of the plurality of external sources into a single application and prioritizing the contingency analysis information in a predetermined order, dynamically updating, the contingency analysis information obtained from each of the plurality of external sources and the prioritization of the contingency analysis information based on status information, and displaying the contingency analysis information to a user via a graphical user interface.

Contingency analysis information for utility service network

Growing community of inventors

Hartsdale, NY, United States of America

Maggie Chow