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Proceedings Paper

Application of neural networks in identification of various types of partial discharges in gas insulated substations
Author(s): K. Krishna Kishore; A. K. Adikesavulu; B. P. Singh; Kumar Eswaran
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Paper 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.

Paper Details

Date Published: 30 March 2000
PDF: 6 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380595
Show Author Affiliations
K. Krishna Kishore, Bharat Heavy Electricals Ltd. (India)
A. K. Adikesavulu, Bharat Heavy Electricals Ltd. (India)
B. P. Singh, Bharat Heavy Electricals Ltd. (India)
Kumar Eswaran, Sri Nidhi Institute of Science and Technology (India)

Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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