Power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model

The reason of the power transformer (PT) monitoring is to prevent the failure of the PT. There are many methods to detect the failure of the PT. The methods include Conventional Monitoring System (CMS), Polarization Depolarization Current (PDC) Analysis, and Hidden Markov Model (HMM). The CMS gives...

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Main Author:	Loo, Yau Teng
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://eprints.utm.my/id/eprint/48586/1/LooYauTengMFKE2014.pdf
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id	my-utm-ep.48586
record_format	uketd_dc
spelling	my-utm-ep.485862017-08-02T07:38:58Z Power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model 2014 Loo, Yau Teng TJ Mechanical engineering and machinery The reason of the power transformer (PT) monitoring is to prevent the failure of the PT. There are many methods to detect the failure of the PT. The methods include Conventional Monitoring System (CMS), Polarization Depolarization Current (PDC) Analysis, and Hidden Markov Model (HMM). The CMS gives the current condition of PT but it cannot give reliable failure prediction. The PDC involves complicated setup at site and the measurement is done when the PT is off line (shutdown) which is not preferable. HMM is a prediction model based on dissolved gas analysis (DGA) database. Its accuracy is believed to be further improved when more DGA data are available with the passing of time. The main focus of this project is to obtain the PT failure time estimates with an error of less than or equal to 10%. Mathematical models were used to predict the PT condition at several stages by knowing the current DGA data. Result shows the accuracy of 90% in transformer level prediction, means that 9 accurate results out of 10 transformers tested. The technique can be used to predict the transformer deterioration level and to prevent transformer failure which can lead to tremendous losses to company. The result will assist the maintenance personnel to make various maintenance decisions with cost effective way. 2014 Thesis http://eprints.utm.my/id/eprint/48586/ http://eprints.utm.my/id/eprint/48586/1/LooYauTengMFKE2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:81881?queryType=vitalDismax&query=Power+transformers+condition+monitoring+using+dissolved+gas+analysis+and+hidden+markov+prediction+model&public=true masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Loo, Yau Teng Power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model
description	The reason of the power transformer (PT) monitoring is to prevent the failure of the PT. There are many methods to detect the failure of the PT. The methods include Conventional Monitoring System (CMS), Polarization Depolarization Current (PDC) Analysis, and Hidden Markov Model (HMM). The CMS gives the current condition of PT but it cannot give reliable failure prediction. The PDC involves complicated setup at site and the measurement is done when the PT is off line (shutdown) which is not preferable. HMM is a prediction model based on dissolved gas analysis (DGA) database. Its accuracy is believed to be further improved when more DGA data are available with the passing of time. The main focus of this project is to obtain the PT failure time estimates with an error of less than or equal to 10%. Mathematical models were used to predict the PT condition at several stages by knowing the current DGA data. Result shows the accuracy of 90% in transformer level prediction, means that 9 accurate results out of 10 transformers tested. The technique can be used to predict the transformer deterioration level and to prevent transformer failure which can lead to tremendous losses to company. The result will assist the maintenance personnel to make various maintenance decisions with cost effective way.
format	Thesis
qualification_level	Master's degree
author	Loo, Yau Teng
author_facet	Loo, Yau Teng
author_sort	Loo, Yau Teng
title	Power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model
title_short	Power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model
title_full	Power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model
title_fullStr	Power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model
title_full_unstemmed	Power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model
title_sort	power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	http://eprints.utm.my/id/eprint/48586/1/LooYauTengMFKE2014.pdf
_version_	1747817426606620672

Power transformers condition monitoring using dissolved gas analysis and hidden markov prediction model

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