System and method employing a self-organizing map load feature database to identify electric load types of different electric loads

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; e...

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Hauptverfasser:	SHARMA SANTOSH K, LU BIN, HARLEY RONALD G, DU LIANG, MADANE MAYURA A, ZAMBARE PRACHI, YANG YI
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ONOTHER TECHNOLOGY AREAS PHYSICS SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORKOPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FORIMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION,DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Beschreibung
Zusammenfassung:	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.