Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset
Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper prop...
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Scientific Research
2014
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| 在線閱讀: | http://eprints.utem.edu.my/id/eprint/16936/2/JCC_2014071111091466.pdf http://eprints.utem.edu.my/id/eprint/16936/ http://www.scirp.org/journal/PaperInformation.aspx?PaperID=47715 http://dx.doi.org/10.4236/jcc.2014.29004 |
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my.utem.eprints.169362021-09-07T03:15:44Z http://eprints.utem.edu.my/id/eprint/16936/ Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset Zahriah, Sahri Rubiyah, Yusof T Technology (General) Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach. Scientific Research 2014-07 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/16936/2/JCC_2014071111091466.pdf Zahriah, Sahri and Rubiyah, Yusof (2014) Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset. Journal Of Computer And Communications, 2. pp. 22-31. ISSN 2327-5219 http://www.scirp.org/journal/PaperInformation.aspx?PaperID=47715 http://dx.doi.org/10.4236/jcc.2014.29004 |
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T Technology (General) |
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T Technology (General) Zahriah, Sahri Rubiyah, Yusof Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset |
| description |
Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach. |
| format |
Article |
| author |
Zahriah, Sahri Rubiyah, Yusof |
| author_facet |
Zahriah, Sahri Rubiyah, Yusof |
| author_sort |
Zahriah, Sahri |
| title |
Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset |
| title_short |
Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset |
| title_full |
Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset |
| title_fullStr |
Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset |
| title_full_unstemmed |
Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset |
| title_sort |
support vector machine based fault diagnosis of power transformer using k nearest neighbor imputed dga dataset |
| publisher |
Scientific Research |
| publishDate |
2014 |
| url |
http://eprints.utem.edu.my/id/eprint/16936/2/JCC_2014071111091466.pdf http://eprints.utem.edu.my/id/eprint/16936/ http://www.scirp.org/journal/PaperInformation.aspx?PaperID=47715 http://dx.doi.org/10.4236/jcc.2014.29004 |
| _version_ |
1710679408688234496 |