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Fault identification in power transformers using dissolve gas analysis and support vector machine

Transformer faults need to be identified accurately at the early stage in order to ease the maintenance of power transformer, reduce the cost of maintenance, avoid severe damage on transformer and extend the lifespan of transformer. Dissolved Gas Analysis (DGA) is the most commonly used method to id...

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Main Authors: Illias, Hazlee Azil, Chan, Kai Choon, Wee, Zhao Liang, Mokhlis, Hazlie, Mohd Ariffin, Azrul, Mohd Yousof, Mohd Fairouz
Format: Conference or Workshop Item
Language:English
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.um.edu.my/35255/1/Profesor%20madya%20Ir.%20Dr.%20Hazlee%20Azil%20bin%20Illias_Fault%20Identification%20in%20Power%20Transformers%20Using.pdf
http://eprints.um.edu.my/35255/
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spelling my.um.eprints.352552023-11-29T06:53:36Z http://eprints.um.edu.my/35255/ Fault identification in power transformers using dissolve gas analysis and support vector machine Illias, Hazlee Azil Chan, Kai Choon Wee, Zhao Liang Mokhlis, Hazlie Mohd Ariffin, Azrul Mohd Yousof, Mohd Fairouz TK Electrical engineering. Electronics Nuclear engineering Transformer faults need to be identified accurately at the early stage in order to ease the maintenance of power transformer, reduce the cost of maintenance, avoid severe damage on transformer and extend the lifespan of transformer. Dissolved Gas Analysis (DGA) is the most commonly used method to identify the transformer fault in power system. However, the existing transformer fault identification methods based on DGA have a limitation because each method is only suitable for certain conditions. Thus, in this work, one of the artificial intelligence techniques, which is Support Vector Machine (SVM), was applied to determine the power transformer fault type based on DGA data. The accuracy of the SVM was tested with different ratio of training and testing data. Comparison of the results from SVM with artificial neural network (ANN) was done to validate the performance of the system. It was found that fault identification in power transformers based on DGA data using SVM yields higher accuracy than ANN. Therefore, SVM can be recommended for the application of power transformer fault type identification in practice. Conference or Workshop Item PeerReviewed text en http://eprints.um.edu.my/35255/1/Profesor%20madya%20Ir.%20Dr.%20Hazlee%20Azil%20bin%20Illias_Fault%20Identification%20in%20Power%20Transformers%20Using.pdf Illias, Hazlee Azil and Chan, Kai Choon and Wee, Zhao Liang and Mokhlis, Hazlie and Mohd Ariffin, Azrul and Mohd Yousof, Mohd Fairouz Fault identification in power transformers using dissolve gas analysis and support vector machine. In: 2021 International Conference on the Properties and Applications of Dielectric Materials, 12-14 July 2021, Kuala Lumpur. (Submitted)
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Illias, Hazlee Azil
Chan, Kai Choon
Wee, Zhao Liang
Mokhlis, Hazlie
Mohd Ariffin, Azrul
Mohd Yousof, Mohd Fairouz
Fault identification in power transformers using dissolve gas analysis and support vector machine
description Transformer faults need to be identified accurately at the early stage in order to ease the maintenance of power transformer, reduce the cost of maintenance, avoid severe damage on transformer and extend the lifespan of transformer. Dissolved Gas Analysis (DGA) is the most commonly used method to identify the transformer fault in power system. However, the existing transformer fault identification methods based on DGA have a limitation because each method is only suitable for certain conditions. Thus, in this work, one of the artificial intelligence techniques, which is Support Vector Machine (SVM), was applied to determine the power transformer fault type based on DGA data. The accuracy of the SVM was tested with different ratio of training and testing data. Comparison of the results from SVM with artificial neural network (ANN) was done to validate the performance of the system. It was found that fault identification in power transformers based on DGA data using SVM yields higher accuracy than ANN. Therefore, SVM can be recommended for the application of power transformer fault type identification in practice.
format Conference or Workshop Item
author Illias, Hazlee Azil
Chan, Kai Choon
Wee, Zhao Liang
Mokhlis, Hazlie
Mohd Ariffin, Azrul
Mohd Yousof, Mohd Fairouz
author_facet Illias, Hazlee Azil
Chan, Kai Choon
Wee, Zhao Liang
Mokhlis, Hazlie
Mohd Ariffin, Azrul
Mohd Yousof, Mohd Fairouz
author_sort Illias, Hazlee Azil
title Fault identification in power transformers using dissolve gas analysis and support vector machine
title_short Fault identification in power transformers using dissolve gas analysis and support vector machine
title_full Fault identification in power transformers using dissolve gas analysis and support vector machine
title_fullStr Fault identification in power transformers using dissolve gas analysis and support vector machine
title_full_unstemmed Fault identification in power transformers using dissolve gas analysis and support vector machine
title_sort fault identification in power transformers using dissolve gas analysis and support vector machine
url http://eprints.um.edu.my/35255/1/Profesor%20madya%20Ir.%20Dr.%20Hazlee%20Azil%20bin%20Illias_Fault%20Identification%20in%20Power%20Transformers%20Using.pdf
http://eprints.um.edu.my/35255/
_version_ 1783945847345512448
score 13.211869

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