Title :
Fault diagnosis system for GIS using an artificial neural network
Author :
Ogi, Hiromi ; Tanaka, Hideo ; Akimoto, Yoshiakira ; Izui, Yoshio
Author_Institution :
Comput. & Commun. Res. Center, Tokyo Electric Power Co., Japan
Abstract :
The authors present an artificial neural network (ANN) approach to a diagnostic system for a gas insulated switchgear (GIS). Firstly they survey the status of operational experience of failures in GISs and its diagnostic techniques. Secondly, they present how to acquire signal samples from the GIS and how to process them so as to be provided for an input layer of ANN. Finally they propose a decision-tree like network referred to as module neural network (MNN), and compare it with the well-known three-layered network, the straight forward neural network (SFNN)
Keywords :
electrical faults; feedforward neural nets; gaseous insulation; power engineering computing; switchgear; ANN; GIS; artificial neural network; decision-tree like network; diagnostic techniques; fault diagnosis system; gas insulated switchgear; module neural network; Artificial neural networks; Assembly; Circuit faults; Fault diagnosis; Gas insulation; Geographic Information Systems; Neural networks; Partial discharges; Pattern classification; Switchgear;
Conference_Titel :
Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0065-3
DOI :
10.1109/ANN.1991.213507
