Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning

In power energy distribution networks, switchgear is considered critical equipment. This because the act of monitoring operation and condition switchgear, as well performing required corrective maintenance on any potentially problematic equipment, critical. A single event may harm thousands customers over time pose a significant risk to operational staff. Many considerations must be put in place before using outages switch down system since they raise costs disrupt supply users. As result, proper interpretation status evaluations for early identification possible faults. Existing ultrasound condition-based (CBM) diagnostic testing techniques require tester manually interpret test data. study aims review recent development CBM with faults switchgear. The current trend electrification will toward safety sustainability grid assets, which involves an evaluation electrical systems’ components’ health grids medium-voltage distribution. work provides state-of-the-art deep learning (DL)-based smart diagnostics that were used identify partial discharges localize them. DL are discussed categorized, special attention given those sophisticated last five years. key features each method, such fundamental approach accuracy, outlined compared depth. benefits drawbacks various algorithms also examined. technological constraints hinder PD from being implemented companies recognized. Lastly, remedies suggested, future research prospects.