A Series Arc Fault Diagnosis Method Based on an Extreme Learning Machine Model Article Swipe
Li Qi
,
Takahiro Kawaguchi
,
Seiji Hashimoto
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/pr12122947
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/pr12122947
In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the extreme learning machine (ELM) model to enhance detection accuracy and real-time monitoring capabilities. Our approach involves collecting high-frequency current signals from 23 types of loads using a self-developed AC series arc fault data acquisition device. We then extract 14 features from both the time and frequency domains as candidates for arc fault diagnosis, employing a random forest to select the most significantly changed features. Finally, we design an ELM classifier for series arc fault diagnosis, achieving an identification accuracy of 99.00% ± 0.26%. Compared to existing series arc fault diagnosis methods, our ELM-based method demonstrates superior recognition performance. This study contributes to the field by providing a more accurate and efficient diagnostic tool for series AC arc faults, with broad implications for electrical safety and fire prevention.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/pr12122947
- https://www.mdpi.com/2227-9717/12/12/2947/pdf?version=1735037128
- OA Status
- gold
- Cited By
- 2
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405730736
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405730736Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/pr12122947Digital Object Identifier
- Title
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A Series Arc Fault Diagnosis Method Based on an Extreme Learning Machine ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-23Full publication date if available
- Authors
-
Li Qi, Takahiro Kawaguchi, Seiji HashimotoList of authors in order
- Landing page
-
https://doi.org/10.3390/pr12122947Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-9717/12/12/2947/pdf?version=1735037128Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2227-9717/12/12/2947/pdf?version=1735037128Direct OA link when available
- Concepts
-
Series (stratigraphy), Extreme learning machine, Computer science, Fault (geology), Arc (geometry), Machine learning, Artificial intelligence, Engineering, Seismology, Geology, Artificial neural network, Mechanical engineering, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.safety | 166 |
| abstract_inverted_index.select | 101 |
| abstract_inverted_index.series | 11, 72, 114, 129, 157 |
| abstract_inverted_index.study, | 2 |
| abstract_inverted_index.address | 4 |
| abstract_inverted_index.changed | 105 |
| abstract_inverted_index.current | 61 |
| abstract_inverted_index.device. | 77 |
| abstract_inverted_index.domains | 89 |
| abstract_inverted_index.enhance | 49 |
| abstract_inverted_index.extract | 80 |
| abstract_inverted_index.extreme | 43 |
| abstract_inverted_index.faults, | 14, 160 |
| abstract_inverted_index.machine | 45 |
| abstract_inverted_index.propose | 35 |
| abstract_inverted_index.signals | 62 |
| abstract_inverted_index.Compared | 126 |
| abstract_inverted_index.Finally, | 107 |
| abstract_inverted_index.accuracy | 51, 121 |
| abstract_inverted_index.accurate | 151 |
| abstract_inverted_index.approach | 57 |
| abstract_inverted_index.critical | 6 |
| abstract_inverted_index.currents | 26 |
| abstract_inverted_index.existing | 128 |
| abstract_inverted_index.features | 82 |
| abstract_inverted_index.identify | 20 |
| abstract_inverted_index.involves | 58 |
| abstract_inverted_index.learning | 44 |
| abstract_inverted_index.methods, | 133 |
| abstract_inverted_index.superior | 138 |
| abstract_inverted_index.ELM-based | 135 |
| abstract_inverted_index.achieving | 118 |
| abstract_inverted_index.detecting | 10 |
| abstract_inverted_index.detection | 50 |
| abstract_inverted_index.diagnosis | 38, 132 |
| abstract_inverted_index.efficient | 153 |
| abstract_inverted_index.employing | 96 |
| abstract_inverted_index.features. | 106 |
| abstract_inverted_index.frequency | 88 |
| abstract_inverted_index.providing | 148 |
| abstract_inverted_index.real-time | 53 |
| abstract_inverted_index.accurately | 9 |
| abstract_inverted_index.candidates | 91 |
| abstract_inverted_index.classifier | 112 |
| abstract_inverted_index.collecting | 59 |
| abstract_inverted_index.diagnosis, | 95, 117 |
| abstract_inverted_index.diagnostic | 154 |
| abstract_inverted_index.electrical | 32, 165 |
| abstract_inverted_index.monitoring | 54 |
| abstract_inverted_index.acquisition | 76 |
| abstract_inverted_index.challenging | 18 |
| abstract_inverted_index.contributes | 143 |
| abstract_inverted_index.devastating | 31 |
| abstract_inverted_index.intelligent | 37 |
| abstract_inverted_index.prevention. | 169 |
| abstract_inverted_index.recognition | 139 |
| abstract_inverted_index.demonstrates | 137 |
| abstract_inverted_index.implications | 163 |
| abstract_inverted_index.performance. | 140 |
| abstract_inverted_index.capabilities. | 55 |
| abstract_inverted_index.significantly | 104 |
| abstract_inverted_index.high-frequency | 60 |
| abstract_inverted_index.identification | 120 |
| abstract_inverted_index.self-developed | 70 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5080497773 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I165735259 |
| citation_normalized_percentile.value | 0.78185791 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |