Data processing: artificial intelligence – Machine learning
Reexamination Certificate
2011-05-17
2011-05-17
Holmes, Michael B. (Department: 2129)
Data processing: artificial intelligence
Machine learning
Reexamination Certificate
active
07945524
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.
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Anderson Roger N.
Arias Marta
Becker Hila
Boulanger Albert
Gross Philip
Baker & Botts L.L.P.
Consolidated Edison of New York, Inc.
Coughlan Peter
Holmes Michael B.
The Trustess of Columbia University in the City of New York
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