Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN
Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) super...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/17/4566 |
| _version_ | 1827706959082553344 |
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| author | Zhe Li Yongpeng Xu Xiuchen Jiang |
| author_facet | Zhe Li Yongpeng Xu Xiuchen Jiang |
| author_sort | Zhe Li |
| collection | DOAJ |
| description | Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range. |
| first_indexed | 2024-03-10T16:35:59Z |
| format | Article |
| id | doaj.art-8922c623a48e45c1a4bed14d72d8c3e7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T16:35:59Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-8922c623a48e45c1a4bed14d72d8c3e72023-11-20T12:26:15ZengMDPI AGEnergies1996-10732020-09-011317456610.3390/en13174566Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBNZhe Li0Yongpeng Xu1Xiuchen Jiang2Academy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaAcademy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaAcademy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaPattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range.https://www.mdpi.com/1996-1073/13/17/4566DC cross linked polyethylene (XLPE) cablepartial discharge (PD)restricted Boltzmann machines (RBM)deep belief network (DBN)adaptive moment estimation (ADAM) |
| spellingShingle | Zhe Li Yongpeng Xu Xiuchen Jiang Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN Energies DC cross linked polyethylene (XLPE) cable partial discharge (PD) restricted Boltzmann machines (RBM) deep belief network (DBN) adaptive moment estimation (ADAM) |
| title | Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN |
| title_full | Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN |
| title_fullStr | Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN |
| title_full_unstemmed | Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN |
| title_short | Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN |
| title_sort | pattern recognition of dc partial discharge on xlpe cable based on adam dbn |
| topic | DC cross linked polyethylene (XLPE) cable partial discharge (PD) restricted Boltzmann machines (RBM) deep belief network (DBN) adaptive moment estimation (ADAM) |
| url | https://www.mdpi.com/1996-1073/13/17/4566 |
| work_keys_str_mv | AT zheli patternrecognitionofdcpartialdischargeonxlpecablebasedonadamdbn AT yongpengxu patternrecognitionofdcpartialdischargeonxlpecablebasedonadamdbn AT xiuchenjiang patternrecognitionofdcpartialdischargeonxlpecablebasedonadamdbn |