Power quality problem classification using wavelet transformation and artificial neural networks

This paper presents a classification method for power quality problems in electrical power systems. To improve the electric power quality, sources of disturbances must be known and controlled. Power quality disturbance waveform recognition is often troublesome because it involves a broad range of di...

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Main Authors:	Kanitpanyacharoean W., Premrudeepreechacharn S.
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Online Access:	http://www.scopus.com/inward/record.url?eid=2-s2.0-27944492026&partnerID=40&md5=cfbdfeb8002176874c1582c06e5646fb http://cmuir.cmu.ac.th/handle/6653943832/1510
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Institution:	Chiang Mai University
Language:	English

id	th-cmuir.6653943832-1510
record_format	dspace
spelling	th-cmuir.6653943832-15102014-08-29T09:29:24Z Power quality problem classification using wavelet transformation and artificial neural networks Kanitpanyacharoean W. Premrudeepreechacharn S. This paper presents a classification method for power quality problems in electrical power systems. To improve the electric power quality, sources of disturbances must be known and controlled. Power quality disturbance waveform recognition is often troublesome because it involves a broad range of disturbance categories or classes. This is a study of power quality problem classification using wavelet transformation and artificial neural networks. After training neural networks, the weight and bias is obtained for using to classify the power quality problems. The combined wavelet transformation with neural networks is able to classify all 6 types for power quality problems correctly. ©2004IEEE. 2014-08-29T09:29:24Z 2014-08-29T09:29:24Z 2004 Conference Paper 66073 85QXA http://www.scopus.com/inward/record.url?eid=2-s2.0-27944492026&partnerID=40&md5=cfbdfeb8002176874c1582c06e5646fb http://cmuir.cmu.ac.th/handle/6653943832/1510 English
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
language	English
description	This paper presents a classification method for power quality problems in electrical power systems. To improve the electric power quality, sources of disturbances must be known and controlled. Power quality disturbance waveform recognition is often troublesome because it involves a broad range of disturbance categories or classes. This is a study of power quality problem classification using wavelet transformation and artificial neural networks. After training neural networks, the weight and bias is obtained for using to classify the power quality problems. The combined wavelet transformation with neural networks is able to classify all 6 types for power quality problems correctly. ©2004IEEE.
format	Conference or Workshop Item
author	Kanitpanyacharoean W. Premrudeepreechacharn S.
spellingShingle	Kanitpanyacharoean W. Premrudeepreechacharn S. Power quality problem classification using wavelet transformation and artificial neural networks
author_facet	Kanitpanyacharoean W. Premrudeepreechacharn S.
author_sort	Kanitpanyacharoean W.
title	Power quality problem classification using wavelet transformation and artificial neural networks
title_short	Power quality problem classification using wavelet transformation and artificial neural networks
title_full	Power quality problem classification using wavelet transformation and artificial neural networks
title_fullStr	Power quality problem classification using wavelet transformation and artificial neural networks
title_full_unstemmed	Power quality problem classification using wavelet transformation and artificial neural networks
title_sort	power quality problem classification using wavelet transformation and artificial neural networks
publishDate	2014
url	http://www.scopus.com/inward/record.url?eid=2-s2.0-27944492026&partnerID=40&md5=cfbdfeb8002176874c1582c06e5646fb http://cmuir.cmu.ac.th/handle/6653943832/1510
_version_	1681419683954163712

Power quality problem classification using wavelet transformation and artificial neural networks

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