WO2013081719A3 - System and method employing a minimum distance and a load feature database to identify electric load types of different electric loads - Google Patents

System and method employing a minimum distance and a load feature database to identify electric load types of different electric loads Download PDF

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Publication number
WO2013081719A3
WO2013081719A3 PCT/US2012/055836 US2012055836W WO2013081719A3 WO 2013081719 A3 WO2013081719 A3 WO 2013081719A3 US 2012055836 W US2012055836 W US 2012055836W WO 2013081719 A3 WO2013081719 A3 WO 2013081719A3
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WO
WIPO (PCT)
Prior art keywords
load
different electric
types
different
minimum distance
Prior art date
Application number
PCT/US2012/055836
Other languages
French (fr)
Other versions
WO2013081719A2 (en
Inventor
Bin Lu
Yi Yang
Santosh K. Sharma
Prachi ZAMBARE
Mayura A. MADANE
Original Assignee
Eaton Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US13/304,834 external-priority patent/US8918346B2/en
Application filed by Eaton Corporation filed Critical Eaton Corporation
Publication of WO2013081719A2 publication Critical patent/WO2013081719A2/en
Publication of WO2013081719A3 publication Critical patent/WO2013081719A3/en

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models

Abstract

A method identifies electric load types (12) of a plurality of different electric loads (22,24,26,28). The method includes providing a load feature database (4) of a plurality of different electric load types (12), each of the different electric load types including a first load feature vector having at least four different load features; sensing a voltage signal (20) and a current signal (18) for each of the different electric loads; determining a second load feature vector (110) comprising at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying (114) by a processor (30) one of the different electric load types by determining a minimum distance of the second load feature vector to the first load feature vector of the different electric load types (12) of the load feature database.
PCT/US2012/055836 2011-11-28 2012-09-18 System and method employing a minimum distance and a load feature database to identify electric load types of different electric loads WO2013081719A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US13/304,834 2011-11-28
US13/304,834 US8918346B2 (en) 2011-11-28 2011-11-28 System and method employing a minimum distance and a load feature database to identify electric load types of different electric loads

Publications (2)

Publication Number Publication Date
WO2013081719A2 WO2013081719A2 (en) 2013-06-06
WO2013081719A3 true WO2013081719A3 (en) 2013-12-12

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Application Number Title Priority Date Filing Date
PCT/US2012/055836 WO2013081719A2 (en) 2011-11-28 2012-09-18 System and method employing a minimum distance and a load feature database to identify electric load types of different electric loads

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WO (1) WO2013081719A2 (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2538087B (en) * 2015-05-06 2019-03-06 Torro Ventures Ltd Analysing a power circuit
WO2016177857A1 (en) * 2015-05-06 2016-11-10 Torro Ventures Limited Analysing a power circuit
DE102018109775A1 (en) 2018-04-24 2019-10-24 Innogy Se Method, device and computer program for designing a battery storage
US11726117B2 (en) 2020-04-30 2023-08-15 Florida Power & Light Company High frequency data transceiver and surge protection retrofit for a smart meter

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2026299A1 (en) * 2007-08-14 2009-02-18 General Electric Company Cognitive electric power meter
WO2011002735A1 (en) * 2009-07-01 2011-01-06 Carnegie Mellon University Methods and apparatuses for monitoring energy consumption and related operations

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2026299A1 (en) * 2007-08-14 2009-02-18 General Electric Company Cognitive electric power meter
WO2011002735A1 (en) * 2009-07-01 2011-01-06 Carnegie Mellon University Methods and apparatuses for monitoring energy consumption and related operations

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
F. KUPZOG, T. ZIA, A. A. ZAIDI: "Automatic electric load identification in self-configuring microgrids", PROCEEDINGS OF THE 9TH IEEE AFRICON CONFERENCE, 23 September 2009 (2009-09-23), XP031558985, DOI: 10.1109/AFRCON.2009.5308129 *
M. ZEIFMAN, K. ROTH: "Nonintrusive appliance load monitoring: review and outlook", IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, vol. 57, no. 1, 21 February 2011 (2011-02-21), pages 76 - 84, XP011478006, DOI: 10.1109/TCE.2011.5735484 *
S. C. CHEN, H. T. YANG: "Implementation of a virtual-instrument for non-intrusive intelligent real-time load monitoring", PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND COMPUTATIONAL INTELLIGENCE (NCI'03), 19 May 2003 (2003-05-19), pages 174 - 178, XP008165018, ISBN: 0-88986-347-4 *
Y. DU, L. DU, B. LU, R. HARLEY, T. HABETLER: "A review of identification and monitoring methods for electric loads in commercial and residential buildings", PROCEEDINGS OF THE 2010 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE'10), 12 September 2010 (2010-09-12), pages 4527 - 4533, XP031787621, DOI: 10.1109/ECCE.2010.5618423 *

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