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.

Date of Patent:
Jun. 17, 2014

Filed:

Nov. 28, 2011
Applicants:

Bin LU, Shanghai, CN;

Ronald G. Harley, Lawrenceville, GA (US);

Liang Du, Atlanta, GA (US);

Yi Yang, Milwaukee, WI (US);

Santosh K. Sharma, Maharashtra, IN;

Prachi Zambare, Maharashtra, IN;

Mayura A. Madane, Maharashtra, IN;

Inventors:

Bin Lu, Shanghai, CN;

Ronald G. Harley, Lawrenceville, GA (US);

Liang Du, Atlanta, GA (US);

Yi Yang, Milwaukee, WI (US);

Santosh K. Sharma, Maharashtra, IN;

Prachi Zambare, Maharashtra, IN;

Mayura A. Madane, Maharashtra, IN;

Assignees:

Eaton Corporation, Cleveland, OH (US);

Georgia Tech Research Corporation, Atlanta, GA (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 17/30 (2006.01);
U.S. Cl.
CPC ...
Abstract

A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.


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