Condition Assessment of Metal Oxide Surge Arrester Based on Multi-Layer SVM Classifier
This paper introduces the indicators for surge arrester condition assessment based on the leakage current analysis. Maximum amplitude of fundamental harmonic of the resistive leakage current, maximum amplitude of third harmonic of the resistive leakage current and maximum amplitude of fundamental ha...
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Iran University of Science and Technology
2015-12-01
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doaj-0b29bf29b1c3485ba381c689a74a23502020-11-24T22:42:42ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902015-12-01114354362Condition Assessment of Metal Oxide Surge Arrester Based on Multi-Layer SVM ClassifierM Khodsuz0M Mirzaie1 Department of Electrical and Computer Department of Electrical and Computer This paper introduces the indicators for surge arrester condition assessment based on the leakage current analysis. Maximum amplitude of fundamental harmonic of the resistive leakage current, maximum amplitude of third harmonic of the resistive leakage current and maximum amplitude of fundamental harmonic of the capacitive leakage current were used as indicators for surge arrester condition monitoring. Also, the effects of operating voltage fluctuation, third harmonic of voltage, overvoltage and surge arrester aging on these indicators were studied. Then, obtained data are applied to the multi-layer support vector machine for recognizing of surge arrester conditions. Obtained results show that introduced indicators have the high ability for evaluation of surge arrester conditions.http://ijeee.iust.ac.ir/browse.php?a_code=A-10-789-4&slc_lang=en&sid=1Metal oxide surge arrester Leakage current Condition monitoring Indicator Multi-layer SVM |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M Khodsuz M Mirzaie |
spellingShingle |
M Khodsuz M Mirzaie Condition Assessment of Metal Oxide Surge Arrester Based on Multi-Layer SVM Classifier Iranian Journal of Electrical and Electronic Engineering Metal oxide surge arrester Leakage current Condition monitoring Indicator Multi-layer SVM |
author_facet |
M Khodsuz M Mirzaie |
author_sort |
M Khodsuz |
title |
Condition Assessment of Metal Oxide Surge Arrester Based on Multi-Layer SVM Classifier |
title_short |
Condition Assessment of Metal Oxide Surge Arrester Based on Multi-Layer SVM Classifier |
title_full |
Condition Assessment of Metal Oxide Surge Arrester Based on Multi-Layer SVM Classifier |
title_fullStr |
Condition Assessment of Metal Oxide Surge Arrester Based on Multi-Layer SVM Classifier |
title_full_unstemmed |
Condition Assessment of Metal Oxide Surge Arrester Based on Multi-Layer SVM Classifier |
title_sort |
condition assessment of metal oxide surge arrester based on multi-layer svm classifier |
publisher |
Iran University of Science and Technology |
series |
Iranian Journal of Electrical and Electronic Engineering |
issn |
1735-2827 2383-3890 |
publishDate |
2015-12-01 |
description |
This paper introduces the indicators for surge arrester condition assessment based on the leakage current analysis. Maximum amplitude of fundamental harmonic of the resistive leakage current, maximum amplitude of third harmonic of the resistive leakage current and maximum amplitude of fundamental harmonic of the capacitive leakage current were used as indicators for surge arrester condition monitoring. Also, the effects of operating voltage fluctuation, third harmonic of voltage, overvoltage and surge arrester aging on these indicators were studied. Then, obtained data are applied to the multi-layer support vector machine for recognizing of surge arrester conditions. Obtained results show that introduced indicators have the high ability for evaluation of surge arrester conditions. |
topic |
Metal oxide surge arrester Leakage current Condition monitoring Indicator Multi-layer SVM |
url |
http://ijeee.iust.ac.ir/browse.php?a_code=A-10-789-4&slc_lang=en&sid=1 |
work_keys_str_mv |
AT mkhodsuz conditionassessmentofmetaloxidesurgearresterbasedonmultilayersvmclassifier AT mmirzaie conditionassessmentofmetaloxidesurgearresterbasedonmultilayersvmclassifier |
_version_ |
1725698900804763648 |