Arcing Faults Detection in Switchgear with Extreme Learning Machine

The robustness of switchgears has critical impacts on the general efficiency of power distribution systems. Faulty switchgears lead to many unwanted complications for utility bodies, which in turn lead to even bigger issues. In this paper, a remote arcing fault sensing technique is proposed using EL...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-08, Vol.2319 (1), p.12007
Main Authors: Ishak, Sanuri, Koh, S.P., Tan, Jian Ding, Tiong, Sieh Kiong, Chen, Chai Phing, Yaw, C.T.
Format: Article
Language:English
Subjects:
Algorithms
Arcing
Artificial neural networks
Electric power distribution
Extreme Learning Machine
Fault detection
Faults
Frequency analysis
Frequency domain analysis
Machine learning
Physics
Remote sensing
Success
Switchgear
Switchgear Faults
Time domain analysis
Training
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Summary:The robustness of switchgears has critical impacts on the general efficiency of power distribution systems. Faulty switchgears lead to many unwanted complications for utility bodies, which in turn lead to even bigger issues. In this paper, a remote arcing fault sensing technique is proposed using ELM. By analysing the sonic waves emitted, the proposed method is capable to detect possible arcing faults in switchgears. Tests and experiments have been conducted to investigate the performance of the proposed algorithm in detecting these arcing faults. The obtained results are analysed in time and frequency domains. In the time domain analysis, the results show 93.75% success rate in training stage, 95.83% in validation stage, and 87.5% in testing stage. In the frequency domain analysis, the results show 93.75% success rate in training stage, 91.67% in validation stage, and 100% success rate in testing stage. It is thus concluded that the proposed algorithm is capable to identify arcing faults in switchgears.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2319/1/012007