Parameter identification for fault detection of power transformer using artificial neural network

Fault diagnosis is a challenging problem because there are numerous fault situations that can possibly occur to an electrical transformer. There are a lot of previous works done by researchers on fault diagnosis in power transformer but all of them used data from Dissolved Gas Analysis (DGA) as inpu...

全面介紹

Saved in:

書目詳細資料
主要作者:	Rosli, Ruzaini
格式:	Thesis
語言:	English
出版:	2015
主題:	TK Electrical engineering Electronics Nuclear engineering
在線閱讀:	http://eprints.utm.my/id/eprint/53960/1/RuzainiRosliMFKE2015.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

id	my-utm-ep.53960
record_format	uketd_dc
spelling	my-utm-ep.539602020-10-08T04:40:18Z Parameter identification for fault detection of power transformer using artificial neural network 2015-06 Rosli, Ruzaini TK Electrical engineering. Electronics Nuclear engineering Fault diagnosis is a challenging problem because there are numerous fault situations that can possibly occur to an electrical transformer. There are a lot of previous works done by researchers on fault diagnosis in power transformer but all of them used data from Dissolved Gas Analysis (DGA) as input for detection. This study will focus on parameter identification that is electrical measurement, which is voltage and current for fault detection due to several limitations of data from DGA that can lead to wrong diagnosis of fault in power transformer. The transformer that been used in this power system model is 132/20 kV with 250 MVA rating. The simulation of nine types of possible fault has been done by MATLAB R2013a Simulink software. To recognize the pattern of fault data, ANN was chosen because of it was easy to apply in power system network and it will work as pattern classifier with the ability to identify fault types accurately. The ANN programming has been done by ANN Pattern Recognition Tool that also in MATLAB R2013a software. It is found that the fault of power transformer can be detected by measuring electrical parameter such as voltage and current and with ANN, detection and classification of fault can be done to diagnose fault in power transformer. After the fault data had been trained for a few times, ANN will learn how to classify it accurately and then it is able to properly resolve new situations which are different from those fault data presented in the learning process. 2015-06 Thesis http://eprints.utm.my/id/eprint/53960/ http://eprints.utm.my/id/eprint/53960/1/RuzainiRosliMFKE2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:86224 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Rosli, Ruzaini Parameter identification for fault detection of power transformer using artificial neural network
description	Fault diagnosis is a challenging problem because there are numerous fault situations that can possibly occur to an electrical transformer. There are a lot of previous works done by researchers on fault diagnosis in power transformer but all of them used data from Dissolved Gas Analysis (DGA) as input for detection. This study will focus on parameter identification that is electrical measurement, which is voltage and current for fault detection due to several limitations of data from DGA that can lead to wrong diagnosis of fault in power transformer. The transformer that been used in this power system model is 132/20 kV with 250 MVA rating. The simulation of nine types of possible fault has been done by MATLAB R2013a Simulink software. To recognize the pattern of fault data, ANN was chosen because of it was easy to apply in power system network and it will work as pattern classifier with the ability to identify fault types accurately. The ANN programming has been done by ANN Pattern Recognition Tool that also in MATLAB R2013a software. It is found that the fault of power transformer can be detected by measuring electrical parameter such as voltage and current and with ANN, detection and classification of fault can be done to diagnose fault in power transformer. After the fault data had been trained for a few times, ANN will learn how to classify it accurately and then it is able to properly resolve new situations which are different from those fault data presented in the learning process.
format	Thesis
qualification_level	Master's degree
author	Rosli, Ruzaini
author_facet	Rosli, Ruzaini
author_sort	Rosli, Ruzaini
title	Parameter identification for fault detection of power transformer using artificial neural network
title_short	Parameter identification for fault detection of power transformer using artificial neural network
title_full	Parameter identification for fault detection of power transformer using artificial neural network
title_fullStr	Parameter identification for fault detection of power transformer using artificial neural network
title_full_unstemmed	Parameter identification for fault detection of power transformer using artificial neural network
title_sort	parameter identification for fault detection of power transformer using artificial neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	http://eprints.utm.my/id/eprint/53960/1/RuzainiRosliMFKE2015.pdf
_version_	1747817661796974592

Parameter identification for fault detection of power transformer using artificial neural network

相似書籍