Fault Diagnosis Of Gas Insulated Switchgear Using Adaptive Neural Networks

In this paper, Artificial Neural Networks (ANNs) approach to diagnostic methods for abnormality detection for Gas Insulated Switchgear (GIS) will be presented. The necessity of predictive maintenance, and current technologies of sensing devices and signal processing are discussed as the introduction. Thereafter, we will show the necessity of adaptive learning to achieve predictive maintenance which plays an important role as signal processing technology. ICLNN(lncrementa1 Cluster Learning Neural Network), that exhibits adaptive learning capability is proposed and evaluated. ICLNN conduct similar functionality as the convcntional clustering algorithm that classifies sensor signal in sclf-organising manner. Brief simulation results of the ICLNN conducted using the data obtained in a factory shows the great possibilities and availabilities of the ICLNN. Keyword: Gas Insulated Switchgear, Artificial Neural Network, ICL, ICLNN, Predictive Maintenance, Abnormality Diagnosis