Recognition of partial discharge using wavelet entropy and neural network for TEV measurement
Partial discharge (PD) is caused by the deterioration of insulation materials. Its detection and accurate measurement are very important to prevent insulation breakdown and catastrophic failures. Detection of PDs in metal-clad apparatus via TEV method is a promising approach in non-intrusive on-line...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/84652 http://hdl.handle.net/10220/12032 |
| _version_ | 1811687619773333504 |
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| author | Luo, Guomin. Zhang, Daming. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Luo, Guomin. Zhang, Daming. |
| author_sort | Luo, Guomin. |
| collection | NTU |
| description | Partial discharge (PD) is caused by the deterioration of insulation materials. Its detection and accurate measurement are very important to prevent insulation breakdown and catastrophic failures. Detection of PDs in metal-clad apparatus via TEV method is a promising approach in non-intrusive on-line tests. However, the electrical interference from background environment is the major barrier of improving its measuring accuracy. The combination of wavelet analysis that reveals local features and entropy that measures disorder can just fulfill the requirements of PD signal analysis and is thus investigated in this paper. Then a wavelet-entropy based PD recognition method is proposed. The pulse features that are characterized by wavelet entropy are employed as the input pattern of a classifier constructed with feed-forward back-propagation neural network. Finally, some PD groups with noisy interferences are tested by trained network. The recognition rate of real PD pulses demonstrates the proposed wavelet-entropy based method is effective in PD signal de-noising. |
| first_indexed | 2024-10-01T05:19:12Z |
| format | Conference Paper |
| id | ntu-10356/84652 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:19:12Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/846522020-03-07T13:24:45Z Recognition of partial discharge using wavelet entropy and neural network for TEV measurement Luo, Guomin. Zhang, Daming. School of Electrical and Electronic Engineering IEEE International Conference on Power System Technology (2012 : Auckland, New Zealand) DRNTU::Engineering::Electrical and electronic engineering Partial discharge (PD) is caused by the deterioration of insulation materials. Its detection and accurate measurement are very important to prevent insulation breakdown and catastrophic failures. Detection of PDs in metal-clad apparatus via TEV method is a promising approach in non-intrusive on-line tests. However, the electrical interference from background environment is the major barrier of improving its measuring accuracy. The combination of wavelet analysis that reveals local features and entropy that measures disorder can just fulfill the requirements of PD signal analysis and is thus investigated in this paper. Then a wavelet-entropy based PD recognition method is proposed. The pulse features that are characterized by wavelet entropy are employed as the input pattern of a classifier constructed with feed-forward back-propagation neural network. Finally, some PD groups with noisy interferences are tested by trained network. The recognition rate of real PD pulses demonstrates the proposed wavelet-entropy based method is effective in PD signal de-noising. 2013-07-23T03:17:47Z 2019-12-06T15:48:57Z 2013-07-23T03:17:47Z 2019-12-06T15:48:57Z 2012 2012 Conference Paper Luo, G., & Zhang, D. (2012). Recognition of partial discharge using wavelet entropy and neural network for TEV measurement. 2012 IEEE International Conference on Power System Technology. https://hdl.handle.net/10356/84652 http://hdl.handle.net/10220/12032 10.1109/PowerCon.2012.6401331 en © 2012 IEEE. |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Luo, Guomin. Zhang, Daming. Recognition of partial discharge using wavelet entropy and neural network for TEV measurement |
| title | Recognition of partial discharge using wavelet entropy and neural network for TEV measurement |
| title_full | Recognition of partial discharge using wavelet entropy and neural network for TEV measurement |
| title_fullStr | Recognition of partial discharge using wavelet entropy and neural network for TEV measurement |
| title_full_unstemmed | Recognition of partial discharge using wavelet entropy and neural network for TEV measurement |
| title_short | Recognition of partial discharge using wavelet entropy and neural network for TEV measurement |
| title_sort | recognition of partial discharge using wavelet entropy and neural network for tev measurement |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/84652 http://hdl.handle.net/10220/12032 |
| work_keys_str_mv | AT luoguomin recognitionofpartialdischargeusingwaveletentropyandneuralnetworkfortevmeasurement AT zhangdaming recognitionofpartialdischargeusingwaveletentropyandneuralnetworkfortevmeasurement |