The Analysis of GIS Defect Model Partial Discharge Test

DOI

10.2991/epee-16.2016.59How to use a DOI?

Keywords

pattern recognition; gas-insulated switchgear; partial discharge; adaptive fuzzy neural inference system

Abstract

In order to achieve the pattern recognition of gas-insulated switchgear GIS defect types of partial discharge, in this paper, the analysis of gas discharge theory and ANSYS simulation software are used to qualitatively analyzed the defect type. Based on the PD characteristics caused by four typical insulation defects, this paper designed corresponding artificial physical discharge models to measure the voltage and to collect data. ANFIS is used to recognize the defect type, and the results show that the application of ANFIS for GIS partial discharge defect type recognition can achieve a satisfying results.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liqiang Liu
AU  - Jiao Wang
PY  - 2016/10
DA  - 2016/10
TI  - The Analysis of GIS Defect Model Partial Discharge Test
BT  - Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering
PB  - Atlantis Press
SP  - 261
EP  - 267
SN  - 2352-5401
UR  - https://doi.org/10.2991/epee-16.2016.59
DO  - 10.2991/epee-16.2016.59
ID  - Liu2016/10
ER  -