Staff View: Assessment of ESDD on high-voltage insulators using artificial neural network

Assessment of ESDD on high-voltage insulators using artificial neural network

The environmental and weather conditions cause flashover on polluted insulators leading to outages in a power system. It is generally recognized that the main causes leading to the contamination of insulators are marine pollution as found in the immediate neighborhood of the coastal regions, and sol...

Full description

Saved in:

Bibliographic Details
Main Authors:	Ahmad A.S., Ghosh P.S., Shahnawaz Ahmed S., Aljunid S.A.K.
Other Authors:	7202040740
Format:	Article
Published:	2023
Subjects:	Atmospheric humidity Electric insulators Electric potential Electric power systems Leakage currents Neural networks Rain Switching Water pollution Economic transfers Equivalent salt deposit density (ESDD) High voltage insulators Wind velocity Salt deposits
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.uniten.dspace-29877
record_format	dspace
spelling	my.uniten.dspace-298772023-12-28T16:58:02Z Assessment of ESDD on high-voltage insulators using artificial neural network Ahmad A.S. Ghosh P.S. Shahnawaz Ahmed S. Aljunid S.A.K. 7202040740 55427760300 57193735776 56025382500 Atmospheric humidity Electric insulators Electric potential Electric power systems Leakage currents Neural networks Rain Switching Water pollution Economic transfers Equivalent salt deposit density (ESDD) High voltage insulators Wind velocity Salt deposits The environmental and weather conditions cause flashover on polluted insulators leading to outages in a power system. It is generally recognized that the main causes leading to the contamination of insulators are marine pollution as found in the immediate neighborhood of the coastal regions, and solid pollution as found in the dense industrial areas. This research is directed towards the study of contamination of insulator under marine pollution. The effects of various meteorological factors on the pollution severity have been investigated thoroughly. A new approach using ANN as a function estimator has been developed and used to model accurately the relationship between ESDD with temperature (T), humidity (H), pressure (P), rainfall (R), and wind velocity (WV). The ANN-predicted ESDDs have been compared with the measured ones for a practical system. � 2004 Elsevier B.V. All rights reserved. Final 2023-12-28T08:58:02Z 2023-12-28T08:58:02Z 2004 Article 10.1016/j.epsr.2004.03.009 2-s2.0-4444288431 https://www.scopus.com/inward/record.uri?eid=2-s2.0-4444288431&doi=10.1016%2fj.epsr.2004.03.009&partnerID=40&md5=4a2de8b7aa8255e7079229963dd22100 https://irepository.uniten.edu.my/handle/123456789/29877 72 2 131 136 Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Atmospheric humidity Electric insulators Electric potential Electric power systems Leakage currents Neural networks Rain Switching Water pollution Economic transfers Equivalent salt deposit density (ESDD) High voltage insulators Wind velocity Salt deposits
spellingShingle	Atmospheric humidity Electric insulators Electric potential Electric power systems Leakage currents Neural networks Rain Switching Water pollution Economic transfers Equivalent salt deposit density (ESDD) High voltage insulators Wind velocity Salt deposits Ahmad A.S. Ghosh P.S. Shahnawaz Ahmed S. Aljunid S.A.K. Assessment of ESDD on high-voltage insulators using artificial neural network
description	The environmental and weather conditions cause flashover on polluted insulators leading to outages in a power system. It is generally recognized that the main causes leading to the contamination of insulators are marine pollution as found in the immediate neighborhood of the coastal regions, and solid pollution as found in the dense industrial areas. This research is directed towards the study of contamination of insulator under marine pollution. The effects of various meteorological factors on the pollution severity have been investigated thoroughly. A new approach using ANN as a function estimator has been developed and used to model accurately the relationship between ESDD with temperature (T), humidity (H), pressure (P), rainfall (R), and wind velocity (WV). The ANN-predicted ESDDs have been compared with the measured ones for a practical system. � 2004 Elsevier B.V. All rights reserved.
author2	7202040740
author_facet	7202040740 Ahmad A.S. Ghosh P.S. Shahnawaz Ahmed S. Aljunid S.A.K.
format	Article
author	Ahmad A.S. Ghosh P.S. Shahnawaz Ahmed S. Aljunid S.A.K.
author_sort	Ahmad A.S.
title	Assessment of ESDD on high-voltage insulators using artificial neural network
title_short	Assessment of ESDD on high-voltage insulators using artificial neural network
title_full	Assessment of ESDD on high-voltage insulators using artificial neural network
title_fullStr	Assessment of ESDD on high-voltage insulators using artificial neural network
title_full_unstemmed	Assessment of ESDD on high-voltage insulators using artificial neural network
title_sort	assessment of esdd on high-voltage insulators using artificial neural network
publishDate	2023
_version_	1806428450050801664
score	13.211869

Assessment of ESDD on high-voltage insulators using artificial neural network

Similar Items