Corona fault detection in switchgear with extreme learning machine
Switchgear is a very important component in a power distribution line. Failure in switchgear can lead to catastrophic danger and losses. In this research, a fault detection system is proposed with the implementation of Extreme Learning Machine (ELM). This algorithm is capable to identify faults in s...
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2023
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my.uniten.dspace-255262023-05-29T16:10:29Z Corona fault detection in switchgear with extreme learning machine Ishak S. Koh S.-P. Tan J.-D. Tiong S.-K. Chen C.-P. 57194057526 22951210700 38863172300 15128307800 25824552100 Switchgear is a very important component in a power distribution line. Failure in switchgear can lead to catastrophic danger and losses. In this research, a fault detection system is proposed with the implementation of Extreme Learning Machine (ELM). This algorithm is capable to identify faults in switchgear by analyzing the sound wave generated. Experiments are carried out to investigate the performance of the developed algorithm in identifying Corona faults in switchgears. The performances are analyzed in time and frequency domains, respectively. In time domain analysis, the results show 90.63%, 87.5%, and 87.5% of success rates in differentiating the Corona and non-Corona cases in training, validation and testing phases respectively. In frequency domain analysis, the results show 89.84%, 83.33%, and 87.5% success rates in training, validation and testing phases respectively. It can thus be concluded that the developed algorithm performed well in identifying Corona faults in switchgears. � 2020, Institute of Advanced Engineering and Science. All rights reserved. Final 2023-05-29T08:10:29Z 2023-05-29T08:10:29Z 2020 Article 10.11591/eei.v9i2.2058 2-s2.0-85083066655 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083066655&doi=10.11591%2feei.v9i2.2058&partnerID=40&md5=f7c963f863a34276061383ce94fbd1f9 https://irepository.uniten.edu.my/handle/123456789/25526 9 2 558 564 All Open Access, Bronze, Green Institute of Advanced Engineering and Science Scopus |
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Switchgear is a very important component in a power distribution line. Failure in switchgear can lead to catastrophic danger and losses. In this research, a fault detection system is proposed with the implementation of Extreme Learning Machine (ELM). This algorithm is capable to identify faults in switchgear by analyzing the sound wave generated. Experiments are carried out to investigate the performance of the developed algorithm in identifying Corona faults in switchgears. The performances are analyzed in time and frequency domains, respectively. In time domain analysis, the results show 90.63%, 87.5%, and 87.5% of success rates in differentiating the Corona and non-Corona cases in training, validation and testing phases respectively. In frequency domain analysis, the results show 89.84%, 83.33%, and 87.5% success rates in training, validation and testing phases respectively. It can thus be concluded that the developed algorithm performed well in identifying Corona faults in switchgears. � 2020, Institute of Advanced Engineering and Science. All rights reserved. |
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57194057526 Ishak S. Koh S.-P. Tan J.-D. Tiong S.-K. Chen C.-P. |
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Ishak S. Koh S.-P. Tan J.-D. Tiong S.-K. Chen C.-P. |
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Ishak S. Koh S.-P. Tan J.-D. Tiong S.-K. Chen C.-P. Corona fault detection in switchgear with extreme learning machine |
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Ishak S. |
title |
Corona fault detection in switchgear with extreme learning machine |
title_short |
Corona fault detection in switchgear with extreme learning machine |
title_full |
Corona fault detection in switchgear with extreme learning machine |
title_fullStr |
Corona fault detection in switchgear with extreme learning machine |
title_full_unstemmed |
Corona fault detection in switchgear with extreme learning machine |
title_sort |
corona fault detection in switchgear with extreme learning machine |
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Institute of Advanced Engineering and Science |
publishDate |
2023 |
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