Abnormality diagnosis of GIS using adaptive resonance theory

Abstract

The paper presents an artificial neural network (ANN) approach using ART2 (Adaptive Resonance Theory 2) to a diagnostic system for gas insulated switchgear (GIS). To begin with, the authors show the background of abnormality diagnosis of GISs from the view point of predictive maintenance of them. Then, they discuss the necessity of ART-type ANNs, as an unsupervised learning method, in which neuron(s) are self-organized and self-created when detecting unexpected signals even if untrained by ANNs through a sensor. Finally, they present brief simulation results and their evaluation.<>