Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms
Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical syst...
Ausführliche Beschreibung
Autor*in: |
Hoang-Long Dang [verfasserIn] Sangshin Kwak [verfasserIn] Seungdeog Choi [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: IEEE Access - IEEE, 2014, 10(2022), Seite 76386-76400 |
---|---|
Übergeordnetes Werk: |
volume:10 ; year:2022 ; pages:76386-76400 |
Links: |
---|
DOI / URN: |
10.1109/ACCESS.2022.3192517 |
---|
Katalog-ID: |
DOAJ020004281 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ020004281 | ||
003 | DE-627 | ||
005 | 20230307034059.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230226s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1109/ACCESS.2022.3192517 |2 doi | |
035 | |a (DE-627)DOAJ020004281 | ||
035 | |a (DE-599)DOAJ081d4b6a84fc4ebcbe5f1cbeaac4b5fc | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a TK1-9971 | |
100 | 0 | |a Hoang-Long Dang |e verfasserin |4 aut | |
245 | 1 | 0 | |a Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors. | ||
650 | 4 | |a Arc diagnosis | |
650 | 4 | |a artificial intelligence | |
650 | 4 | |a DC arc failure | |
650 | 4 | |a identifying arc fault | |
653 | 0 | |a Electrical engineering. Electronics. Nuclear engineering | |
700 | 0 | |a Sangshin Kwak |e verfasserin |4 aut | |
700 | 0 | |a Seungdeog Choi |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t IEEE Access |d IEEE, 2014 |g 10(2022), Seite 76386-76400 |w (DE-627)728440385 |w (DE-600)2687964-5 |x 21693536 |7 nnns |
773 | 1 | 8 | |g volume:10 |g year:2022 |g pages:76386-76400 |
856 | 4 | 0 | |u https://doi.org/10.1109/ACCESS.2022.3192517 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/081d4b6a84fc4ebcbe5f1cbeaac4b5fc |z kostenfrei |
856 | 4 | 0 | |u https://ieeexplore.ieee.org/document/9833500/ |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2169-3536 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 10 |j 2022 |h 76386-76400 |
author_variant |
h l d hld s k sk s c sc |
---|---|
matchkey_str |
article:21693536:2022----::dniyndsreadaallrsaeode |
hierarchy_sort_str |
2022 |
callnumber-subject-code |
TK |
publishDate |
2022 |
allfields |
10.1109/ACCESS.2022.3192517 doi (DE-627)DOAJ020004281 (DE-599)DOAJ081d4b6a84fc4ebcbe5f1cbeaac4b5fc DE-627 ger DE-627 rakwb eng TK1-9971 Hoang-Long Dang verfasserin aut Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors. Arc diagnosis artificial intelligence DC arc failure identifying arc fault Electrical engineering. Electronics. Nuclear engineering Sangshin Kwak verfasserin aut Seungdeog Choi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 76386-76400 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:76386-76400 https://doi.org/10.1109/ACCESS.2022.3192517 kostenfrei https://doaj.org/article/081d4b6a84fc4ebcbe5f1cbeaac4b5fc kostenfrei https://ieeexplore.ieee.org/document/9833500/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 76386-76400 |
spelling |
10.1109/ACCESS.2022.3192517 doi (DE-627)DOAJ020004281 (DE-599)DOAJ081d4b6a84fc4ebcbe5f1cbeaac4b5fc DE-627 ger DE-627 rakwb eng TK1-9971 Hoang-Long Dang verfasserin aut Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors. Arc diagnosis artificial intelligence DC arc failure identifying arc fault Electrical engineering. Electronics. Nuclear engineering Sangshin Kwak verfasserin aut Seungdeog Choi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 76386-76400 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:76386-76400 https://doi.org/10.1109/ACCESS.2022.3192517 kostenfrei https://doaj.org/article/081d4b6a84fc4ebcbe5f1cbeaac4b5fc kostenfrei https://ieeexplore.ieee.org/document/9833500/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 76386-76400 |
allfields_unstemmed |
10.1109/ACCESS.2022.3192517 doi (DE-627)DOAJ020004281 (DE-599)DOAJ081d4b6a84fc4ebcbe5f1cbeaac4b5fc DE-627 ger DE-627 rakwb eng TK1-9971 Hoang-Long Dang verfasserin aut Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors. Arc diagnosis artificial intelligence DC arc failure identifying arc fault Electrical engineering. Electronics. Nuclear engineering Sangshin Kwak verfasserin aut Seungdeog Choi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 76386-76400 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:76386-76400 https://doi.org/10.1109/ACCESS.2022.3192517 kostenfrei https://doaj.org/article/081d4b6a84fc4ebcbe5f1cbeaac4b5fc kostenfrei https://ieeexplore.ieee.org/document/9833500/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 76386-76400 |
allfieldsGer |
10.1109/ACCESS.2022.3192517 doi (DE-627)DOAJ020004281 (DE-599)DOAJ081d4b6a84fc4ebcbe5f1cbeaac4b5fc DE-627 ger DE-627 rakwb eng TK1-9971 Hoang-Long Dang verfasserin aut Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors. Arc diagnosis artificial intelligence DC arc failure identifying arc fault Electrical engineering. Electronics. Nuclear engineering Sangshin Kwak verfasserin aut Seungdeog Choi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 76386-76400 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:76386-76400 https://doi.org/10.1109/ACCESS.2022.3192517 kostenfrei https://doaj.org/article/081d4b6a84fc4ebcbe5f1cbeaac4b5fc kostenfrei https://ieeexplore.ieee.org/document/9833500/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 76386-76400 |
allfieldsSound |
10.1109/ACCESS.2022.3192517 doi (DE-627)DOAJ020004281 (DE-599)DOAJ081d4b6a84fc4ebcbe5f1cbeaac4b5fc DE-627 ger DE-627 rakwb eng TK1-9971 Hoang-Long Dang verfasserin aut Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors. Arc diagnosis artificial intelligence DC arc failure identifying arc fault Electrical engineering. Electronics. Nuclear engineering Sangshin Kwak verfasserin aut Seungdeog Choi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 76386-76400 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:76386-76400 https://doi.org/10.1109/ACCESS.2022.3192517 kostenfrei https://doaj.org/article/081d4b6a84fc4ebcbe5f1cbeaac4b5fc kostenfrei https://ieeexplore.ieee.org/document/9833500/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 76386-76400 |
language |
English |
source |
In IEEE Access 10(2022), Seite 76386-76400 volume:10 year:2022 pages:76386-76400 |
sourceStr |
In IEEE Access 10(2022), Seite 76386-76400 volume:10 year:2022 pages:76386-76400 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Arc diagnosis artificial intelligence DC arc failure identifying arc fault Electrical engineering. Electronics. Nuclear engineering |
isfreeaccess_bool |
true |
container_title |
IEEE Access |
authorswithroles_txt_mv |
Hoang-Long Dang @@aut@@ Sangshin Kwak @@aut@@ Seungdeog Choi @@aut@@ |
publishDateDaySort_date |
2022-01-01T00:00:00Z |
hierarchy_top_id |
728440385 |
id |
DOAJ020004281 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ020004281</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307034059.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2022.3192517</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ020004281</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ081d4b6a84fc4ebcbe5f1cbeaac4b5fc</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hoang-Long Dang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Arc diagnosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DC arc failure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">identifying arc fault</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sangshin Kwak</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Seungdeog Choi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">10(2022), Seite 76386-76400</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:76386-76400</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2022.3192517</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/081d4b6a84fc4ebcbe5f1cbeaac4b5fc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9833500/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="h">76386-76400</subfield></datafield></record></collection>
|
callnumber-first |
T - Technology |
author |
Hoang-Long Dang |
spellingShingle |
Hoang-Long Dang misc TK1-9971 misc Arc diagnosis misc artificial intelligence misc DC arc failure misc identifying arc fault misc Electrical engineering. Electronics. Nuclear engineering Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms |
authorStr |
Hoang-Long Dang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)728440385 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
TK1-9971 |
illustrated |
Not Illustrated |
issn |
21693536 |
topic_title |
TK1-9971 Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms Arc diagnosis artificial intelligence DC arc failure identifying arc fault |
topic |
misc TK1-9971 misc Arc diagnosis misc artificial intelligence misc DC arc failure misc identifying arc fault misc Electrical engineering. Electronics. Nuclear engineering |
topic_unstemmed |
misc TK1-9971 misc Arc diagnosis misc artificial intelligence misc DC arc failure misc identifying arc fault misc Electrical engineering. Electronics. Nuclear engineering |
topic_browse |
misc TK1-9971 misc Arc diagnosis misc artificial intelligence misc DC arc failure misc identifying arc fault misc Electrical engineering. Electronics. Nuclear engineering |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
IEEE Access |
hierarchy_parent_id |
728440385 |
hierarchy_top_title |
IEEE Access |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)728440385 (DE-600)2687964-5 |
title |
Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms |
ctrlnum |
(DE-627)DOAJ020004281 (DE-599)DOAJ081d4b6a84fc4ebcbe5f1cbeaac4b5fc |
title_full |
Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms |
author_sort |
Hoang-Long Dang |
journal |
IEEE Access |
journalStr |
IEEE Access |
callnumber-first-code |
T |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
txt |
container_start_page |
76386 |
author_browse |
Hoang-Long Dang Sangshin Kwak Seungdeog Choi |
container_volume |
10 |
class |
TK1-9971 |
format_se |
Elektronische Aufsätze |
author-letter |
Hoang-Long Dang |
doi_str_mv |
10.1109/ACCESS.2022.3192517 |
author2-role |
verfasserin |
title_sort |
identifying dc series and parallel arcs based on deep learning algorithms |
callnumber |
TK1-9971 |
title_auth |
Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms |
abstract |
Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors. |
abstractGer |
Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors. |
abstract_unstemmed |
Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
title_short |
Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms |
url |
https://doi.org/10.1109/ACCESS.2022.3192517 https://doaj.org/article/081d4b6a84fc4ebcbe5f1cbeaac4b5fc https://ieeexplore.ieee.org/document/9833500/ https://doaj.org/toc/2169-3536 |
remote_bool |
true |
author2 |
Sangshin Kwak Seungdeog Choi |
author2Str |
Sangshin Kwak Seungdeog Choi |
ppnlink |
728440385 |
callnumber-subject |
TK - Electrical and Nuclear Engineering |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1109/ACCESS.2022.3192517 |
callnumber-a |
TK1-9971 |
up_date |
2024-07-04T01:45:20.557Z |
_version_ |
1803611034677673984 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ020004281</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307034059.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2022.3192517</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ020004281</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ081d4b6a84fc4ebcbe5f1cbeaac4b5fc</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hoang-Long Dang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Arc diagnosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DC arc failure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">identifying arc fault</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Sangshin Kwak</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Seungdeog Choi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">10(2022), Seite 76386-76400</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:76386-76400</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2022.3192517</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/081d4b6a84fc4ebcbe5f1cbeaac4b5fc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9833500/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="h">76386-76400</subfield></datafield></record></collection>
|
score |
7.399167 |