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To improve the insulation diagnosis of a medium-voltage (MV) switchgear, we have developed a four-terminal surface current sensor and a method to estimate the direction of partial discharge occurrence. In this method, the accuracy of the direction estimation is improved by referring only to the direct wave, and the error due to noise can be suppressed by calculating the outputs of the four terminals. The experimental results demonstrate that the detection characteristics of the sensor concur well with the theoretical characteristics derived from the detection principle. Additionally, we conducted an experiment using the developed sensor and the new method to detect the surface current generated due to partial discharge occurring in a rectangular enclosure. The results demonstrate that the occurrence direction could be estimated even for a rectangular enclosure. Furthermore, the propagation attenuation of the surface current at the corners of the enclosure was large, exhibiting an attenuation 0.25 times for the worst case. Finally, it was confirmed that noise removal using a wavelet transform could reduce the direction estimation error of the discharge occurrence by approximately half. [ABSTRACT FROM AUTHOR]

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