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Neural network approach of harmonics detection

This paper describes a novel approach of harmonics detection in a power system which can be used as an alternative to the conventional approaches. The proposed approach uses the multilayer feed forward neural network to determine the harmonic components in a six-pulse bridge converter. In this paper...

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Main Authors: Zin, A.A.M, Rokonuzzaman, M., Shaibon, H., Lo, K.I.
Format: Article
Language:English
Published: 1998
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/1975/1/Zin1998_NeuralNetworkApproachOfHarmonicsDetection.pdf
http://eprints.utm.my/id/eprint/1975/
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id my.utm.1975
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spelling my.utm.19752010-06-01T03:00:01Z http://eprints.utm.my/id/eprint/1975/ Neural network approach of harmonics detection Zin, A.A.M Rokonuzzaman, M. Shaibon, H. Lo, K.I. TK Electrical engineering. Electronics Nuclear engineering This paper describes a novel approach of harmonics detection in a power system which can be used as an alternative to the conventional approaches. The proposed approach uses the multilayer feed forward neural network to determine the harmonic components in a six-pulse bridge converter. In this paper the detection of 5th, 7th, and 11th harmonic components from the distorted waves has been verified by means of the computer simulation. It is found that once trained by the learning algorithm, the neural network can determine each harmonic component very effectively and efficiently 1998-03-03 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/1975/1/Zin1998_NeuralNetworkApproachOfHarmonicsDetection.pdf Zin, A.A.M and Rokonuzzaman, M. and Shaibon, H. and Lo, K.I. (1998) Neural network approach of harmonics detection. Energy Management and Power Delivery, 1998. Proceedings of EMPD '98. 1998 International Conference on , 2 . pp. 467-472.
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/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Zin, A.A.M
Rokonuzzaman, M.
Shaibon, H.
Lo, K.I.
Neural network approach of harmonics detection
description This paper describes a novel approach of harmonics detection in a power system which can be used as an alternative to the conventional approaches. The proposed approach uses the multilayer feed forward neural network to determine the harmonic components in a six-pulse bridge converter. In this paper the detection of 5th, 7th, and 11th harmonic components from the distorted waves has been verified by means of the computer simulation. It is found that once trained by the learning algorithm, the neural network can determine each harmonic component very effectively and efficiently
format Article
author Zin, A.A.M
Rokonuzzaman, M.
Shaibon, H.
Lo, K.I.
author_facet Zin, A.A.M
Rokonuzzaman, M.
Shaibon, H.
Lo, K.I.
author_sort Zin, A.A.M
title Neural network approach of harmonics detection
title_short Neural network approach of harmonics detection
title_full Neural network approach of harmonics detection
title_fullStr Neural network approach of harmonics detection
title_full_unstemmed Neural network approach of harmonics detection
title_sort neural network approach of harmonics detection
publishDate 1998
url http://eprints.utm.my/id/eprint/1975/1/Zin1998_NeuralNetworkApproachOfHarmonicsDetection.pdf
http://eprints.utm.my/id/eprint/1975/
_version_ 1643643467810734080
score 13.211869

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