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Detection of corona faults in switchgear by ssing 1D-CNN, LSTM, and 1D-CNN-LSTM methods_CQU.pdf (2.77 MB)

Detection of corona faults in switchgear by ssing 1D-CNN, LSTM, and 1D-CNN-LSTM methods

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posted on 2024-05-27, 19:48 authored by YA Mohammed Alsumaidaee, CT Yaw, SP Koh, SK Tiong, CP Chen, Talal YusafTalal Yusaf, AN Abdalla, K Ali, AA Raj
The damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains.

History

Volume

23

Issue

6

Start Page

1

End Page

19

Number of Pages

19

eISSN

1424-8220

ISSN

1424-8220

Publisher

MDPI AG

Publisher License

CC BY

Additional Rights

CC BY 4.0 DEED

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2023-03-01

Era Eligible

  • Yes

Medium

Electronic

Journal

Sensors

Article Number

3108

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