GIS internal fault diagnostics using artificial neural networks

Author

Izui, Yoshio

Author_Institution

Mitsubishi Electr. Corp., Hyogo, Japan

Volume

1

fYear

1999

fDate

31 Jan-4 Feb 1999

Firstpage

350

Abstract

This panel describes the application of ANN to internal fault detection of GIS (gas insulated switchgear). The goal of this application is the predictive maintenance of the system. The GIS is monitored on-line through attached-sensors to detect small symptoms of abnormalities before a fatal malfunction. A new method of ANN architecture called ICLNN (incremental cluster learning neural network) is employed to perform recognition of patterns to the averaged spectrum of sensor signals. The working of the prototype system is demonstrated with some experimental results to illustrate the advantages of the ANN

Keywords

fault diagnosis; gas insulated switchgear; maintenance engineering; neural nets; pattern recognition; power engineering computing; GIS internal fault diagnostics; artificial neural networks; attached-sensors; gas insulated switchgear; incremental cluster learning neural network; internal fault detection; pattern recognition; predictive maintenance; Artificial neural networks; Fault detection; Gas insulation; Geographic Information Systems; Monitoring; Neural networks; Pattern recognition; Predictive maintenance; Prototypes; Switchgear;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering Society 1999 Winter Meeting, IEEE

Conference_Location

New York, NY

Print_ISBN

0-7803-4893-1

Type

conf

DOI

10.1109/PESW.1999.747477

Filename

747477