Machine learning for power grid


Ontology type: sgo:Patent     


Patent Info

DATE

N/A

AUTHORS

Roger N. Anderson , Albert Boulanger , Cynthia Rudin , David Waltz , Ansaf Salleb-Aouissi , Maggie Chow , Haimonti Dutta , Phil Gross , Huang Bert , Steve Ierome , Delfina Isaac , Arthur Kressner , Rebecca J. Passonneau , Axinia Radeva , Leon L. Wu , Peter Hofmann , Frank Dougherty

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. More... »

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