Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms
An oil-filled transformer should be able to operate for a long time with proper maintenance. One of the best diagnostic methods for oil-immersed transformer condition is dissolved gas analysis (DGA). However, there are times where the produce of stray gassing event might lead to fault indication in...
| Main Authors: | , , , , |
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| Format: | Article |
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Springer
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/100700/ |
| _version_ | 1848863392847101952 |
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| author | Haw, Jia Yong Mohd Yousof, Mohd Fairouz Abd Rahman, Rahisham Talib, Mohd Aizam Azis, Norhafiz |
| author_facet | Haw, Jia Yong Mohd Yousof, Mohd Fairouz Abd Rahman, Rahisham Talib, Mohd Aizam Azis, Norhafiz |
| author_sort | Haw, Jia Yong |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | An oil-filled transformer should be able to operate for a long time with proper maintenance. One of the best diagnostic methods for oil-immersed transformer condition is dissolved gas analysis (DGA). However, there are times where the produce of stray gassing event might lead to fault indication in the transformer. Machine learning algorithms are used to classify the DGA data into normal condition and corresponding faults based on IEEE limits and Duval pentagon method. The algorithms that will be used include boosted trees, RUS boosted trees and subspace KNN, which belongs to the same ensemble group. Data resampling technique (SMOTETomek) is applied and shows further improvement on the accuracy of predictions by machine learning algorithms when deal with imbalance data. The algorithms are able to achieve the accuracy of 82.6% (boosted trees), 81.2% (RUS boosted trees) and 72.5% (subspace KNN), respectively, when validated with actual transformer condition. |
| first_indexed | 2025-11-15T13:32:12Z |
| format | Article |
| id | upm-100700 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:32:12Z |
| publishDate | 2022 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1007002023-09-15T04:07:08Z http://psasir.upm.edu.my/id/eprint/100700/ Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms Haw, Jia Yong Mohd Yousof, Mohd Fairouz Abd Rahman, Rahisham Talib, Mohd Aizam Azis, Norhafiz An oil-filled transformer should be able to operate for a long time with proper maintenance. One of the best diagnostic methods for oil-immersed transformer condition is dissolved gas analysis (DGA). However, there are times where the produce of stray gassing event might lead to fault indication in the transformer. Machine learning algorithms are used to classify the DGA data into normal condition and corresponding faults based on IEEE limits and Duval pentagon method. The algorithms that will be used include boosted trees, RUS boosted trees and subspace KNN, which belongs to the same ensemble group. Data resampling technique (SMOTETomek) is applied and shows further improvement on the accuracy of predictions by machine learning algorithms when deal with imbalance data. The algorithms are able to achieve the accuracy of 82.6% (boosted trees), 81.2% (RUS boosted trees) and 72.5% (subspace KNN), respectively, when validated with actual transformer condition. Springer 2022-04-01 Article PeerReviewed Haw, Jia Yong and Mohd Yousof, Mohd Fairouz and Abd Rahman, Rahisham and Talib, Mohd Aizam and Azis, Norhafiz (2022) Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms. Arabian Journal for Science and Engineering, 47 (11). pp. 14355-14364. ISSN 2193-567X; ESSN: 2191-4281 https://link.springer.com/article/10.1007/s13369-022-06770-0 10.1007/s13369-022-06770-0 |
| spellingShingle | Haw, Jia Yong Mohd Yousof, Mohd Fairouz Abd Rahman, Rahisham Talib, Mohd Aizam Azis, Norhafiz Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms |
| title | Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms |
| title_full | Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms |
| title_fullStr | Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms |
| title_full_unstemmed | Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms |
| title_short | Classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms |
| title_sort | classification of fault and stray gassing in transformer by using duval pentagon and machine learning algorithms |
| url | http://psasir.upm.edu.my/id/eprint/100700/ http://psasir.upm.edu.my/id/eprint/100700/ http://psasir.upm.edu.my/id/eprint/100700/ |