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Prediction of flashover voltage of contaminated insulator using artificial neural networks

Insulator contamination has been identified as the most important factor in the design of external insulation of high voltage transmission, sub-transmission and distribution systems throughout the world. In the electrical design of high voltage insulators, the design engineers need a simple and reli...

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Main Authors: Alawi, Saleh Al, Salam, M. A., Maqrashi, A. A., Ahmad, Hussein
Format: Article
Published: Taylor & Francis 2006
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/9041/
http://dx.doi.org/10.1080/15325000600561563
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spelling my.utm.90412018-03-07T21:10:38Z http://eprints.utm.my/id/eprint/9041/ Prediction of flashover voltage of contaminated insulator using artificial neural networks Alawi, Saleh Al Salam, M. A. Maqrashi, A. A. Ahmad, Hussein TK Electrical engineering. Electronics Nuclear engineering Insulator contamination has been identified as the most important factor in the design of external insulation of high voltage transmission, sub-transmission and distribution systems throughout the world. In the electrical design of high voltage insulators, the design engineers need a simple and reliable tool when calculating the flashover voltages of contaminated insulators. This article presents an artificial neural network (ANN) based technique that predicts the flashover voltages of the insulator under contaminated conditions energized by AC voltage. The results indicate strong agreement between the model prediction and observed values. The statistical analysis shows that the R 2 value for the sixteen cases in the training set was 0.9986. These results demonstrate that the ANN-based model developed in this work can predict the flashover voltage and ESDD, before and after applying a mitigation system, with 99.86% accuracy and with 99.3%, respectively. It was also found that the contribution of the salinity level was approximately 46.51%; the effect of the solution current was 31.78%, while the remaining 21.71% was attributed to the resistivity. These results clearly indicate that salinity is an important factor in determining ESDD and FOV, and its level should be determined carefully. Taylor & Francis 2006 Article PeerReviewed Alawi, Saleh Al and Salam, M. A. and Maqrashi, A. A. and Ahmad, Hussein (2006) Prediction of flashover voltage of contaminated insulator using artificial neural networks. Electric Power Components and Systems, 34 (8). pp. 831-840. ISSN 1532-5008 http://dx.doi.org/10.1080/15325000600561563 10.1080/15325000600561563
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Alawi, Saleh Al
Salam, M. A.
Maqrashi, A. A.
Ahmad, Hussein
Prediction of flashover voltage of contaminated insulator using artificial neural networks
description Insulator contamination has been identified as the most important factor in the design of external insulation of high voltage transmission, sub-transmission and distribution systems throughout the world. In the electrical design of high voltage insulators, the design engineers need a simple and reliable tool when calculating the flashover voltages of contaminated insulators. This article presents an artificial neural network (ANN) based technique that predicts the flashover voltages of the insulator under contaminated conditions energized by AC voltage. The results indicate strong agreement between the model prediction and observed values. The statistical analysis shows that the R 2 value for the sixteen cases in the training set was 0.9986. These results demonstrate that the ANN-based model developed in this work can predict the flashover voltage and ESDD, before and after applying a mitigation system, with 99.86% accuracy and with 99.3%, respectively. It was also found that the contribution of the salinity level was approximately 46.51%; the effect of the solution current was 31.78%, while the remaining 21.71% was attributed to the resistivity. These results clearly indicate that salinity is an important factor in determining ESDD and FOV, and its level should be determined carefully.
format Article
author Alawi, Saleh Al
Salam, M. A.
Maqrashi, A. A.
Ahmad, Hussein
author_facet Alawi, Saleh Al
Salam, M. A.
Maqrashi, A. A.
Ahmad, Hussein
author_sort Alawi, Saleh Al
title Prediction of flashover voltage of contaminated insulator using artificial neural networks
title_short Prediction of flashover voltage of contaminated insulator using artificial neural networks
title_full Prediction of flashover voltage of contaminated insulator using artificial neural networks
title_fullStr Prediction of flashover voltage of contaminated insulator using artificial neural networks
title_full_unstemmed Prediction of flashover voltage of contaminated insulator using artificial neural networks
title_sort prediction of flashover voltage of contaminated insulator using artificial neural networks
publisher Taylor & Francis
publishDate 2006
url http://eprints.utm.my/id/eprint/9041/
http://dx.doi.org/10.1080/15325000600561563
_version_ 1643645103840952320
score 13.211869

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