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The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 11144858 B1

PDFFull Text
Date of Patent:
Oct. 12, 2021

Filed:

Sep. 09, 2019

Machine learning of electrical system behavior, and related systems, apparatuses, and methods

Applicant:

Enel X North America, Inc., Boston, MA (US);

Inventor:

John Michael Fife, Bend, OR (US);

Assignee:

Enel X North America, Inc., Boston, MA (US);

Attorneys:

Stoel Rives LLP

R. Whitney Johnson

Primary Examiner:

Ziaul Karim

Int. Cl.
CPC ...
G06Q 10/06 (2012.01); G01R 31/367 (2019.01); G01R 31/392 (2019.01); H02J 3/28 (2006.01); H02J 3/46 (2006.01); H02J 13/00 (2006.01); H02J 7/00 (2006.01); G06Q 30/02 (2012.01); G05B 13/02 (2006.01); G06Q 50/06 (2012.01); H02J 3/14 (2006.01); G05B 17/02 (2006.01); G05B 13/04 (2006.01); G05F 1/66 (2006.01); H02J 3/00 (2006.01);
U.S. Cl.
CPC ...
G06Q 10/06313 (2013.01); G01R 31/367 (2019.01); G01R 31/392 (2019.01); G05B 13/021 (2013.01); G05B 13/0265 (2013.01); G05B 13/041 (2013.01); G05B 13/048 (2013.01); G05B 17/02 (2013.01); G05F 1/66 (2013.01); G06Q 10/06315 (2013.01); G06Q 30/0206 (2013.01); G06Q 50/06 (2013.01); H02J 3/00 (2013.01); H02J 3/14 (2013.01); H02J 3/28 (2013.01); H02J 3/46 (2013.01); H02J 7/007 (2013.01); H02J 7/0063 (2013.01); H02J 13/00 (2013.01); H02J 13/0006 (2013.01); H02J 13/0079 (2013.01); H02J 7/0025 (2020.01); H02J 13/00001 (2020.01); H02J 2007/0067 (2013.01); H02J 2203/20 (2020.01); H02J 2310/64 (2020.01); Y02B 10/30 (2013.01); Y02B 70/3225 (2013.01); Y04S 20/222 (2013.01);
Abstract

The present disclosure is directed to machine learning of electrical power system behavior, and related systems, apparatuses, and methods. A controller of an electrical power system includes a data storage device configured to store model data indicating a model load power consumed by loads of the electrical power system. The controller also includes a processor configured to determine current data including current load power consumed by the loads, modify the model data by aggregating the model data with the current data, and determine a set of control values for a set of control variables to effectuate a change to operation of the electrical power system based, at least in part, on the model data.

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