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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Institute of Electrical and Electronics Engineers Inc.
2023
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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 |
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| 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 |
| score |
13.222552 |