Frequency variations measurement techniques in power quality monitoring

Power quality is a term used to describe electric power that motivates an electrical load and the load's ability to function properly with that electric power. Without the proper power, an electrical device load may malfunction, fail prematurely or not operate at all. There are many ways in whi...

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Main Author: Wood, Terence
Format: Thesis
Language:English
Published: 2008
Subjects:
Online Access:http://eprints.utm.my/9588/1/TerenceWoodMFKE2008.pdf
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author Wood, Terence
author_facet Wood, Terence
author_sort Wood, Terence
collection ePrints
description Power quality is a term used to describe electric power that motivates an electrical load and the load's ability to function properly with that electric power. Without the proper power, an electrical device load may malfunction, fail prematurely or not operate at all. There are many ways in which electric power can be of poor quality and many more causes of such poor quality power. The aim of this thesis is to study measurement techniques available in monitoring power system frequency particularly using the Least Square Error (LSE) method and neural network approach (ADALINE). The algorithms for the various measurement methods in this thesis were implemented using MATLAB. Results concluded that the neural network approach (ADALINE) is much better for frequency approximation although the LES algorithm provides an easier measurement method. The LES technique was found to be not accurate in the presence of noise and harmonics. Further studies could be done on recursive LES which may compensate the drawbacks of non-recursive LES algorithm which was studied in this work.
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spelling utm.eprints-95882018-07-19T01:53:08Z http://eprints.utm.my/9588/ Frequency variations measurement techniques in power quality monitoring Wood, Terence TK Electrical engineering. Electronics Nuclear engineering Power quality is a term used to describe electric power that motivates an electrical load and the load's ability to function properly with that electric power. Without the proper power, an electrical device load may malfunction, fail prematurely or not operate at all. There are many ways in which electric power can be of poor quality and many more causes of such poor quality power. The aim of this thesis is to study measurement techniques available in monitoring power system frequency particularly using the Least Square Error (LSE) method and neural network approach (ADALINE). The algorithms for the various measurement methods in this thesis were implemented using MATLAB. Results concluded that the neural network approach (ADALINE) is much better for frequency approximation although the LES algorithm provides an easier measurement method. The LES technique was found to be not accurate in the presence of noise and harmonics. Further studies could be done on recursive LES which may compensate the drawbacks of non-recursive LES algorithm which was studied in this work. 2008-05 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/9588/1/TerenceWoodMFKE2008.pdf Wood, Terence (2008) Frequency variations measurement techniques in power quality monitoring. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Wood, Terence
Frequency variations measurement techniques in power quality monitoring
title Frequency variations measurement techniques in power quality monitoring
title_full Frequency variations measurement techniques in power quality monitoring
title_fullStr Frequency variations measurement techniques in power quality monitoring
title_full_unstemmed Frequency variations measurement techniques in power quality monitoring
title_short Frequency variations measurement techniques in power quality monitoring
title_sort frequency variations measurement techniques in power quality monitoring
topic TK Electrical engineering. Electronics Nuclear engineering
url http://eprints.utm.my/9588/1/TerenceWoodMFKE2008.pdf
work_keys_str_mv AT woodterence frequencyvariationsmeasurementtechniquesinpowerqualitymonitoring