Lifetime prediction and estimation of power transformer

This project presents a method to estimate and predict failure probability related to aging transformer units in power system. Statistically, the sample mean or the average age method is acceptable if it is used in a case where there is a big population. This method obviously is not suitable f...

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Main Author:	Oon, Syahira Raihan
Format:	Thesis
Language:	English English English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.uthm.edu.my/2134/1/24p%20SYAHIRA%20RAIHAN%20OON.pdf http://eprints.uthm.edu.my/2134/2/SYAHIRA%20RAIHAN%20OON%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2134/3/SYAHIRA%20RAIHAN%20OON%20WATERMARK.pdf
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id	my-uthm-ep.2134
record_format	uketd_dc
spelling	my-uthm-ep.21342021-10-31T02:43:45Z Lifetime prediction and estimation of power transformer 2013-01 Oon, Syahira Raihan TK Electrical engineering. Electronics Nuclear engineering TK2000-2891 Dynamoelectric machinery and auxiliaries. Including generators, motors, transformers This project presents a method to estimate and predict failure probability related to aging transformer units in power system. Statistically, the sample mean or the average age method is acceptable if it is used in a case where there is a big population. This method obviously is not suitable for power system components with very few samples of end-of-life failures. The essential weakness of the sample mean is that it only uses information of died components. This research proposed an approach to estimate and predict the lifetime of a power transformer by using NGC data. The data with both died and survive transformers will make contribution on estimating the mean life of power transformer. Two methods that used are normal and Weibull distributions. Although the two methods have different estimation approaches and solution techniques, they are related to each other and use the same format of raw data. From this research, the mean life and standard deviation for normal and Weibull distribution estimation should be quite close and also to the shape of the both distribution. Thus, statistical reliability analysis can provide predictions, such percentage of transformer that will fail at a particular time of before a particular age and how many transformers will fail in the next future year by using failure rate model. From that prediction, the forecasted capital expenditure ( the cost of replacement and consequential failure cost) also can be specified. Thus, it will avoid asset harvesting and the possibility of having unforeseen costs. 2013-01 Thesis http://eprints.uthm.edu.my/2134/ http://eprints.uthm.edu.my/2134/1/24p%20SYAHIRA%20RAIHAN%20OON.pdf text en public http://eprints.uthm.edu.my/2134/2/SYAHIRA%20RAIHAN%20OON%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/2134/3/SYAHIRA%20RAIHAN%20OON%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TK Electrical engineering Electronics Nuclear engineering TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering TK Electrical engineering Electronics Nuclear engineering Oon, Syahira Raihan Lifetime prediction and estimation of power transformer
description	This project presents a method to estimate and predict failure probability related to aging transformer units in power system. Statistically, the sample mean or the average age method is acceptable if it is used in a case where there is a big population. This method obviously is not suitable for power system components with very few samples of end-of-life failures. The essential weakness of the sample mean is that it only uses information of died components. This research proposed an approach to estimate and predict the lifetime of a power transformer by using NGC data. The data with both died and survive transformers will make contribution on estimating the mean life of power transformer. Two methods that used are normal and Weibull distributions. Although the two methods have different estimation approaches and solution techniques, they are related to each other and use the same format of raw data. From this research, the mean life and standard deviation for normal and Weibull distribution estimation should be quite close and also to the shape of the both distribution. Thus, statistical reliability analysis can provide predictions, such percentage of transformer that will fail at a particular time of before a particular age and how many transformers will fail in the next future year by using failure rate model. From that prediction, the forecasted capital expenditure ( the cost of replacement and consequential failure cost) also can be specified. Thus, it will avoid asset harvesting and the possibility of having unforeseen costs.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Oon, Syahira Raihan
author_facet	Oon, Syahira Raihan
author_sort	Oon, Syahira Raihan
title	Lifetime prediction and estimation of power transformer
title_short	Lifetime prediction and estimation of power transformer
title_full	Lifetime prediction and estimation of power transformer
title_fullStr	Lifetime prediction and estimation of power transformer
title_full_unstemmed	Lifetime prediction and estimation of power transformer
title_sort	lifetime prediction and estimation of power transformer
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2013
url	http://eprints.uthm.edu.my/2134/1/24p%20SYAHIRA%20RAIHAN%20OON.pdf http://eprints.uthm.edu.my/2134/2/SYAHIRA%20RAIHAN%20OON%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2134/3/SYAHIRA%20RAIHAN%20OON%20WATERMARK.pdf
_version_	1747830913989869568

Lifetime prediction and estimation of power transformer

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