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

fYear

1991

fDate

23-26 Jul 1991

Firstpage

112

Lastpage

116

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ANN.1991.213507

Filename

213507