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

摘要

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.

主权项

1. A method of identifying electric load types of a plurality of different electric loads, said method comprising:
providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of said different electric load types corresponding to a number of said neurons; employing a weight vector for each of said neurons; sensing a voltage signal and a current signal for each of said different electric loads; determining a load feature vector comprising at least four different load features from said sensed voltage signal and said sensed current signal for a corresponding one of said different electric loads; identifying by a processor one of said different electric load types by relating the load feature vector to the neurons of said self-organizing map load feature database by identifying the weight vector of one of said neurons corresponding to said one of said different electric load types that is a minimal distance to the load feature vector; employing with said identifying the weight vector an average squared Euclidean distance to a plurality of neurons in a class corresponding to said one of said plurality of different electric load types; employing i as an index; employing ωi as said class; employing x as said load feature vector; for each of said plurality of different electric load types, employing a group of values of said self-organizing map load feature database having a mean yi and a square covariance matrix Σi; employing with said identifying the weight vector a point-to-cluster function of average squared Euclidean distance from said load feature vector to every point in said class, ωi, corresponding to said one of said plurality of different electric load types; employing Tr( ) as a trace of the square covariance matrix Σi; and determining the average squared Euclidean distance from:
dE(x,ωi)2=(x−yi)T(x−yi)+Tr(Σi).