Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification
Abstract Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Iwata, Shinya [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Electrical engineering - Springer Berlin Heidelberg, 1994, 103(2021), 6 vom: 09. Mai, Seite 3189-3199 |
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| Übergeordnetes Werk: |
volume:103 ; year:2021 ; number:6 ; day:09 ; month:05 ; pages:3189-3199 |
| Links: |
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| DOI / URN: |
10.1007/s00202-021-01306-5 |
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OLC2077470348 |
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| 520 | |a Abstract Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning classification for different types of experimental electrode systems. A polyethylene sheet was used as the sample, and PD was generated by applying a high AC voltage to four different types of electrodes. The number of PD pulses was counted from the raw data as preprocessing to calculate the feature value for the machine learning. The PD generation rate was defined for each phase angle section. Four types of machine learning algorithms were adopted for the classification of the electrode system: k-NN (k-nearest neighbor algorithm), logistic regression, decision tree, and random forest. The best accuracy was obtained by using the random forest algorithm (0.97), and it was found that k-NN also demonstrated good performance. The parameter dependencies were also evaluated for each algorithm. Based on the results generated by the random forest, it became clear that there were phase angle sections that were of high importance. The reason for the result was discussed from the perspective of (i) the difference due to the electrical circuit parameters (modified abc-model) and (ii) the stochastic fluctuation of PD signal. | ||
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10.1007/s00202-021-01306-5 doi (DE-627)OLC2077470348 (DE-He213)s00202-021-01306-5-p DE-627 ger DE-627 rakwb eng 621.3 VZ 620 VZ Iwata, Shinya verfasserin (orcid)0000-0002-5431-457X aut Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning classification for different types of experimental electrode systems. A polyethylene sheet was used as the sample, and PD was generated by applying a high AC voltage to four different types of electrodes. The number of PD pulses was counted from the raw data as preprocessing to calculate the feature value for the machine learning. The PD generation rate was defined for each phase angle section. Four types of machine learning algorithms were adopted for the classification of the electrode system: k-NN (k-nearest neighbor algorithm), logistic regression, decision tree, and random forest. The best accuracy was obtained by using the random forest algorithm (0.97), and it was found that k-NN also demonstrated good performance. The parameter dependencies were also evaluated for each algorithm. Based on the results generated by the random forest, it became clear that there were phase angle sections that were of high importance. The reason for the result was discussed from the perspective of (i) the difference due to the electrical circuit parameters (modified abc-model) and (ii) the stochastic fluctuation of PD signal. Partial discharge Phase-resolved partial discharge Machine learning Random forest Kitani, Ryota (orcid)0000-0001-7151-2933 aut Enthalten in Electrical engineering Springer Berlin Heidelberg, 1994 103(2021), 6 vom: 09. Mai, Seite 3189-3199 (DE-627)182588734 (DE-600)1219035-4 (DE-576)045292310 0948-7921 nnns volume:103 year:2021 number:6 day:09 month:05 pages:3189-3199 https://doi.org/10.1007/s00202-021-01306-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_24 GBV_ILN_207 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_4277 AR 103 2021 6 09 05 3189-3199 |
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10.1007/s00202-021-01306-5 doi (DE-627)OLC2077470348 (DE-He213)s00202-021-01306-5-p DE-627 ger DE-627 rakwb eng 621.3 VZ 620 VZ Iwata, Shinya verfasserin (orcid)0000-0002-5431-457X aut Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning classification for different types of experimental electrode systems. A polyethylene sheet was used as the sample, and PD was generated by applying a high AC voltage to four different types of electrodes. The number of PD pulses was counted from the raw data as preprocessing to calculate the feature value for the machine learning. The PD generation rate was defined for each phase angle section. Four types of machine learning algorithms were adopted for the classification of the electrode system: k-NN (k-nearest neighbor algorithm), logistic regression, decision tree, and random forest. The best accuracy was obtained by using the random forest algorithm (0.97), and it was found that k-NN also demonstrated good performance. The parameter dependencies were also evaluated for each algorithm. Based on the results generated by the random forest, it became clear that there were phase angle sections that were of high importance. The reason for the result was discussed from the perspective of (i) the difference due to the electrical circuit parameters (modified abc-model) and (ii) the stochastic fluctuation of PD signal. Partial discharge Phase-resolved partial discharge Machine learning Random forest Kitani, Ryota (orcid)0000-0001-7151-2933 aut Enthalten in Electrical engineering Springer Berlin Heidelberg, 1994 103(2021), 6 vom: 09. Mai, Seite 3189-3199 (DE-627)182588734 (DE-600)1219035-4 (DE-576)045292310 0948-7921 nnns volume:103 year:2021 number:6 day:09 month:05 pages:3189-3199 https://doi.org/10.1007/s00202-021-01306-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_24 GBV_ILN_207 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_4277 AR 103 2021 6 09 05 3189-3199 |
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10.1007/s00202-021-01306-5 doi (DE-627)OLC2077470348 (DE-He213)s00202-021-01306-5-p DE-627 ger DE-627 rakwb eng 621.3 VZ 620 VZ Iwata, Shinya verfasserin (orcid)0000-0002-5431-457X aut Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning classification for different types of experimental electrode systems. A polyethylene sheet was used as the sample, and PD was generated by applying a high AC voltage to four different types of electrodes. The number of PD pulses was counted from the raw data as preprocessing to calculate the feature value for the machine learning. The PD generation rate was defined for each phase angle section. Four types of machine learning algorithms were adopted for the classification of the electrode system: k-NN (k-nearest neighbor algorithm), logistic regression, decision tree, and random forest. The best accuracy was obtained by using the random forest algorithm (0.97), and it was found that k-NN also demonstrated good performance. The parameter dependencies were also evaluated for each algorithm. Based on the results generated by the random forest, it became clear that there were phase angle sections that were of high importance. The reason for the result was discussed from the perspective of (i) the difference due to the electrical circuit parameters (modified abc-model) and (ii) the stochastic fluctuation of PD signal. Partial discharge Phase-resolved partial discharge Machine learning Random forest Kitani, Ryota (orcid)0000-0001-7151-2933 aut Enthalten in Electrical engineering Springer Berlin Heidelberg, 1994 103(2021), 6 vom: 09. Mai, Seite 3189-3199 (DE-627)182588734 (DE-600)1219035-4 (DE-576)045292310 0948-7921 nnns volume:103 year:2021 number:6 day:09 month:05 pages:3189-3199 https://doi.org/10.1007/s00202-021-01306-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_24 GBV_ILN_207 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_4277 AR 103 2021 6 09 05 3189-3199 |
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Iwata, Shinya Kitani, Ryota |
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10.1007/s00202-021-01306-5 |
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phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification |
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Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification |
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Abstract Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning classification for different types of experimental electrode systems. A polyethylene sheet was used as the sample, and PD was generated by applying a high AC voltage to four different types of electrodes. The number of PD pulses was counted from the raw data as preprocessing to calculate the feature value for the machine learning. The PD generation rate was defined for each phase angle section. Four types of machine learning algorithms were adopted for the classification of the electrode system: k-NN (k-nearest neighbor algorithm), logistic regression, decision tree, and random forest. The best accuracy was obtained by using the random forest algorithm (0.97), and it was found that k-NN also demonstrated good performance. The parameter dependencies were also evaluated for each algorithm. Based on the results generated by the random forest, it became clear that there were phase angle sections that were of high importance. The reason for the result was discussed from the perspective of (i) the difference due to the electrical circuit parameters (modified abc-model) and (ii) the stochastic fluctuation of PD signal. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning classification for different types of experimental electrode systems. A polyethylene sheet was used as the sample, and PD was generated by applying a high AC voltage to four different types of electrodes. The number of PD pulses was counted from the raw data as preprocessing to calculate the feature value for the machine learning. The PD generation rate was defined for each phase angle section. Four types of machine learning algorithms were adopted for the classification of the electrode system: k-NN (k-nearest neighbor algorithm), logistic regression, decision tree, and random forest. The best accuracy was obtained by using the random forest algorithm (0.97), and it was found that k-NN also demonstrated good performance. The parameter dependencies were also evaluated for each algorithm. Based on the results generated by the random forest, it became clear that there were phase angle sections that were of high importance. The reason for the result was discussed from the perspective of (i) the difference due to the electrical circuit parameters (modified abc-model) and (ii) the stochastic fluctuation of PD signal. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning classification for different types of experimental electrode systems. A polyethylene sheet was used as the sample, and PD was generated by applying a high AC voltage to four different types of electrodes. The number of PD pulses was counted from the raw data as preprocessing to calculate the feature value for the machine learning. The PD generation rate was defined for each phase angle section. Four types of machine learning algorithms were adopted for the classification of the electrode system: k-NN (k-nearest neighbor algorithm), logistic regression, decision tree, and random forest. The best accuracy was obtained by using the random forest algorithm (0.97), and it was found that k-NN also demonstrated good performance. The parameter dependencies were also evaluated for each algorithm. Based on the results generated by the random forest, it became clear that there were phase angle sections that were of high importance. The reason for the result was discussed from the perspective of (i) the difference due to the electrical circuit parameters (modified abc-model) and (ii) the stochastic fluctuation of PD signal. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification |
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