Application of Neural Networks in identification of various type of partial discharges in gas insulated substation

被引：0

作者：

Kishore, KK ^{[1
]}

Adikesavulu, AK ^{[1
]}

Singh, BP ^{[1
]}

Eswaran, K ^{[1
]}

机构：

[1] BHEL Corp R&D, Hyderabad, Andhra Pradesh, India

来源：

APPLICATIONS AND SCIENCE OF COMPUTATIONAL INTELLIGENCE III | 2000年 / 4055卷

关键词：

D O I：

10.1117/12.380595

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Gas Insulated substations (GIS) upto 500kV class have been widely accepted over conventional air insulated substation due to several advantages. However, the presence of floating metal particles and protrusions within the GIS at various locations could seriously affect the performance. The paper describes the method of detection of partial discharges for various type of discharging sources e.g. floating particles, protrusions on high voltage conductor and particles sticking on the surface of insulator. In order to identify the discharge source, a Neural Network programme is developed to classify each of the above source on the basis of its characteristic pattern.

引用

页码：411 / 416

页数：6

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