An Advanced Partial Discharge Recognition Strategy of Power Cable
Detection and localization of partial discharge are very important in condition monitoring of power cables, so it is necessary to build an accurate recognizer to recognize the discharge types. In this paper, firstly, a power cable model based on FDTD simulation is built to get the typical discharge...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/174538 |
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| _version_ | 1849412898188689408 |
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| author | Xiaotian Bi Ang Ren Simeng Li Mingming Han Qingquan Li |
| author_facet | Xiaotian Bi Ang Ren Simeng Li Mingming Han Qingquan Li |
| author_sort | Xiaotian Bi |
| collection | DOAJ |
| description | Detection and localization of partial discharge are very important in condition monitoring of power cables, so it is necessary to build an accurate recognizer to recognize the discharge types. In this paper, firstly, a power cable model based on FDTD simulation is built to get the typical discharge signals as training samples. Secondly, because the extraction of discharge signal features is crucial, fractal characteristics of the training samples are extracted and inputted into the recognizer. To make the results more accurate, multi-SVM recognizer made up of six Support Vector Machines (SVM) is proposed in this paper. The result of the multi-SVM recognizer is determined by the vote of the six SVM. Finally, the BP neural networks and ELM are compared with multi-SVM. The accuracy comparison shows that the multi-SVM recognizer has the best accuracy and stability, and it can recognize the discharge type efficiently. |
| format | Article |
| id | doaj-art-4ba47f85ec0a42c9a6d7850decd1f30f |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-4ba47f85ec0a42c9a6d7850decd1f30f2025-08-20T03:34:18ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552015-01-01201510.1155/2015/174538174538An Advanced Partial Discharge Recognition Strategy of Power CableXiaotian Bi0Ang Ren1Simeng Li2Mingming Han3Qingquan Li4Shandong Provincial Key Laboratory of UHV Transmission Technology & Equipment, School of Electrical Engineering, Shandong University, Jinan 250061, ChinaShandong Provincial Key Laboratory of UHV Transmission Technology & Equipment, School of Electrical Engineering, Shandong University, Jinan 250061, ChinaShandong Provincial Key Laboratory of UHV Transmission Technology & Equipment, School of Electrical Engineering, Shandong University, Jinan 250061, ChinaShandong Provincial Key Laboratory of UHV Transmission Technology & Equipment, School of Electrical Engineering, Shandong University, Jinan 250061, ChinaShandong Provincial Key Laboratory of UHV Transmission Technology & Equipment, School of Electrical Engineering, Shandong University, Jinan 250061, ChinaDetection and localization of partial discharge are very important in condition monitoring of power cables, so it is necessary to build an accurate recognizer to recognize the discharge types. In this paper, firstly, a power cable model based on FDTD simulation is built to get the typical discharge signals as training samples. Secondly, because the extraction of discharge signal features is crucial, fractal characteristics of the training samples are extracted and inputted into the recognizer. To make the results more accurate, multi-SVM recognizer made up of six Support Vector Machines (SVM) is proposed in this paper. The result of the multi-SVM recognizer is determined by the vote of the six SVM. Finally, the BP neural networks and ELM are compared with multi-SVM. The accuracy comparison shows that the multi-SVM recognizer has the best accuracy and stability, and it can recognize the discharge type efficiently.http://dx.doi.org/10.1155/2015/174538 |
| spellingShingle | Xiaotian Bi Ang Ren Simeng Li Mingming Han Qingquan Li An Advanced Partial Discharge Recognition Strategy of Power Cable Journal of Electrical and Computer Engineering |
| title | An Advanced Partial Discharge Recognition Strategy of Power Cable |
| title_full | An Advanced Partial Discharge Recognition Strategy of Power Cable |
| title_fullStr | An Advanced Partial Discharge Recognition Strategy of Power Cable |
| title_full_unstemmed | An Advanced Partial Discharge Recognition Strategy of Power Cable |
| title_short | An Advanced Partial Discharge Recognition Strategy of Power Cable |
| title_sort | advanced partial discharge recognition strategy of power cable |
| url | http://dx.doi.org/10.1155/2015/174538 |
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