Fault Identification in Power Transformers Using Dissolve Gas Analysis and Support Vector Machine

Neural networks; Power transformers; Support vector machines; Artificial intelligence techniques; Cost of maintenance; Dissolved gas analyses (DGA); Fault identifications; Life span; Training and testing; Transformer faults; Dielectric materials

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Bibliographic Details
Main Authors:	Illias H.A., Kai Choon C., Liang W.Z., Mokhlis H., Ariffin A.M., Fairouz Mohd Yousof M.
Other Authors:	26633053900
Format:	Conference Paper
Published:	Institute of Electrical and Electronics Engineers Inc. 2023
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Institution:	Universiti Tenaga Nasional

id	my.uniten.dspace-26100
record_format	dspace
spelling	my.uniten.dspace-261002023-05-29T17:06:48Z Fault Identification in Power Transformers Using Dissolve Gas Analysis and Support Vector Machine Illias H.A. Kai Choon C. Liang W.Z. Mokhlis H. Ariffin A.M. Fairouz Mohd Yousof M. 26633053900 57226568375 57200387475 8136874200 16400722400 36547024400 Neural networks; Power transformers; Support vector machines; Artificial intelligence techniques; Cost of maintenance; Dissolved gas analyses (DGA); Fault identifications; Life span; Training and testing; Transformer faults; Dielectric materials 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. � 2021 IEEE. Final 2023-05-29T09:06:48Z 2023-05-29T09:06:48Z 2021 Conference Paper 10.1109/ICPADM49635.2021.9493970 2-s2.0-85111961755 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111961755&doi=10.1109%2fICPADM49635.2021.9493970&partnerID=40&md5=99d08f1a9a8688adbf4aceb52c91b759 https://irepository.uniten.edu.my/handle/123456789/26100 2021-July 9493970 33 36 Institute of Electrical and Electronics Engineers Inc. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Neural networks; Power transformers; Support vector machines; Artificial intelligence techniques; Cost of maintenance; Dissolved gas analyses (DGA); Fault identifications; Life span; Training and testing; Transformer faults; Dielectric materials
author2	26633053900
author_facet	26633053900 Illias H.A. Kai Choon C. Liang W.Z. Mokhlis H. Ariffin A.M. Fairouz Mohd Yousof M.
format	Conference Paper
author	Illias H.A. Kai Choon C. Liang W.Z. Mokhlis H. Ariffin A.M. Fairouz Mohd Yousof M.
spellingShingle	Illias H.A. Kai Choon C. Liang W.Z. Mokhlis H. Ariffin A.M. Fairouz Mohd Yousof M. Fault Identification in Power Transformers Using Dissolve Gas Analysis and Support Vector Machine
author_sort	Illias H.A.
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
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2023
_version_	1806428233512517632

Fault Identification in Power Transformers Using Dissolve Gas Analysis and Support Vector Machine

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