Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

Yaseen Ahmed Mohammed Alsumaidaee; Chong Tak Yaw; Siaw Paw Koh; Sieh Kiong Tiong; Chai Phing Chen; Talal Yusaf; Ahmed N Abdalla; Kharudin Ali; Avinash Ashwin Raj

doi:10.3390/s23063108

Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

Sensors (Basel). 2023 Mar 14;23(6):3108. doi: 10.3390/s23063108.

Authors

Affiliations

¹ College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia.
² Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia.
³ Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia.
⁴ School of Engineering and Technology, Central Queensland University, Brisbane, QLD 4009, Australia.
⁵ Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an 223003, China.
⁶ Faculty of Electrical and Automation Engineering Technology, UC TATI, Teluk Kalong, Kemaman 24000, Terengganu, Malaysia.
⁷ Tenaga National Berhard Research Sdn. Bhd., No. 1, Kawasan Institusi Penyelidikan, Jln Ayer Hitam, Kajang 43000, Selangor, Malaysia.

Abstract

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.

Keywords: 1D-CNN-LSTM; corona discharge; energy; faults; switchgear.

Abstract

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