Power Transformer Classification through Dissolved Gas Analysis Utilizing Least-Square Support Vector Machine

- This article proposes the utilization of the Least-Square Support Vector Machine (LS-SVM) approach to ascertain the presence of a fault in power transformers. Power transformers are essential elements of electrical power systems. The failure of a power transformer can cause a disturbance in the fu...

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Published in:SSRG International Journal of Electrical and Electronics Engineering
Main Author: Yasin Z.M.; Zakaria F.; Salim N.A.; Ab Aziz N.F.
Format: Article
Language:English
Published: Seventh Sense Research Group 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185768631&doi=10.14445%2f23488379%2fIJEEE-V11I2P107&partnerID=40&md5=04f05fb5da2f34c10fdb23198dd37a08
id 2-s2.0-85185768631
spelling 2-s2.0-85185768631
Yasin Z.M.; Zakaria F.; Salim N.A.; Ab Aziz N.F.
Power Transformer Classification through Dissolved Gas Analysis Utilizing Least-Square Support Vector Machine
2024
SSRG International Journal of Electrical and Electronics Engineering
11
2
10.14445/23488379/IJEEE-V11I2P107
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185768631&doi=10.14445%2f23488379%2fIJEEE-V11I2P107&partnerID=40&md5=04f05fb5da2f34c10fdb23198dd37a08
- This article proposes the utilization of the Least-Square Support Vector Machine (LS-SVM) approach to ascertain the presence of a fault in power transformers. Power transformers are essential elements of electrical power systems. The failure of a power transformer can cause a disturbance in the functioning of power distribution and transmission systems. This situation will result in an increase in operating expenses due to the need for repairs and maintenance. The reliability of the electrical grid may be compromised. Therefore, it is crucial to identify any flaws in the power transformer at an early stage. In this paper, the LS-SVM utilizes Dissolved Gas Analysis (DGA) data as its input. The DGA methodology is widely accepted as the prevailing method for identifying the early stages of defects that arise in power transformers by analyzing the ratio of essential gases. The simulation data acquired from the industry comprises a standard state and six distinct fault types of transformers, which are utilized as input for the LS-SVM models. The suggested model underwent testing in multiple scenarios, yielding a maximum accuracy of 97.37%. © 2024 Seventh Sense Research Group. All rights reserved.
Seventh Sense Research Group
23488379
English
Article
All Open Access; Hybrid Gold Open Access
author Yasin Z.M.; Zakaria F.; Salim N.A.; Ab Aziz N.F.
spellingShingle Yasin Z.M.; Zakaria F.; Salim N.A.; Ab Aziz N.F.
Power Transformer Classification through Dissolved Gas Analysis Utilizing Least-Square Support Vector Machine
author_facet Yasin Z.M.; Zakaria F.; Salim N.A.; Ab Aziz N.F.
author_sort Yasin Z.M.; Zakaria F.; Salim N.A.; Ab Aziz N.F.
title Power Transformer Classification through Dissolved Gas Analysis Utilizing Least-Square Support Vector Machine
title_short Power Transformer Classification through Dissolved Gas Analysis Utilizing Least-Square Support Vector Machine
title_full Power Transformer Classification through Dissolved Gas Analysis Utilizing Least-Square Support Vector Machine
title_fullStr Power Transformer Classification through Dissolved Gas Analysis Utilizing Least-Square Support Vector Machine
title_full_unstemmed Power Transformer Classification through Dissolved Gas Analysis Utilizing Least-Square Support Vector Machine
title_sort Power Transformer Classification through Dissolved Gas Analysis Utilizing Least-Square Support Vector Machine
publishDate 2024
container_title SSRG International Journal of Electrical and Electronics Engineering
container_volume 11
container_issue 2
doi_str_mv 10.14445/23488379/IJEEE-V11I2P107
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185768631&doi=10.14445%2f23488379%2fIJEEE-V11I2P107&partnerID=40&md5=04f05fb5da2f34c10fdb23198dd37a08
description - This article proposes the utilization of the Least-Square Support Vector Machine (LS-SVM) approach to ascertain the presence of a fault in power transformers. Power transformers are essential elements of electrical power systems. The failure of a power transformer can cause a disturbance in the functioning of power distribution and transmission systems. This situation will result in an increase in operating expenses due to the need for repairs and maintenance. The reliability of the electrical grid may be compromised. Therefore, it is crucial to identify any flaws in the power transformer at an early stage. In this paper, the LS-SVM utilizes Dissolved Gas Analysis (DGA) data as its input. The DGA methodology is widely accepted as the prevailing method for identifying the early stages of defects that arise in power transformers by analyzing the ratio of essential gases. The simulation data acquired from the industry comprises a standard state and six distinct fault types of transformers, which are utilized as input for the LS-SVM models. The suggested model underwent testing in multiple scenarios, yielding a maximum accuracy of 97.37%. © 2024 Seventh Sense Research Group. All rights reserved.
publisher Seventh Sense Research Group
issn 23488379
language English
format Article
accesstype All Open Access; Hybrid Gold Open Access
record_format scopus
collection Scopus
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