<title>Application of neural networks in identification of various types of partial discharges in gas insulated substations</title>

Abstract

Gas Insulated substations (GIS) up to 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 of high voltage conductor and particles sticking on the surface of insulator. In order to identify the discharge source, a Neural Network program is developed to classify each of the above source on the basis of its characteristic pattern.