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

• Main Authors: Mohammed Alsumaidaee Y.A., Yaw C.T., Koh S.P., Tiong S.K., Chen C.P., Yusaf T., Abdalla A.N., Ali K., Raj A.A.
• Other Authors: 58648412900
• Format: Article
• Published: MDPI 2024
• Subjects: 1D-CNN-LSTM; corona discharge; energy; faults; switchgear; Air quality; Electric corona; Fault detection; Flashover; Learning systems; Long short-term memory; Time domain analysis; 1d-CNN-LSTM; Corona discharges; Damaging effects; Electrical equipment; Electrical stress; Energy; Fault; Medium voltage; Metal-clad; Time and frequency domains; algorithm; article; deep learning; human; learning; long short term memory network; physiological stress; sound; worker; Frequency domain analysis
• Institution: Universiti Tenaga Nasional

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...