Recent Advances and Research in Online Monitoring Pattern Recognition of Cable Partial Discharge Based on Computer Technology

Abstract

This article provides a detailed review of recent advancements and significant achievements in the field of cable partial discharge online monitoring pattern recognition. It examines the evolution of monitoring technologies and the integration of advanced data analytics that have contributed to more accurate and reliable identification of partial discharge patterns. The article also addresses the existing challenges in partial discharge pattern recognition, such as the difficulties in distinguishing between various types of discharges under complex operational conditions and the need for more robust algorithms capable of handling large datasets with high noise levels. To overcome these challenges, potential solutions are proposed, including the application of machine learning techniques, the development of enhanced signal processing methods, and the use of more sensitive and precise sensors.Furthermore, the article explores the implications of these advancements for engineering practices, particularly in the context of predictive maintenance and the early detection of faults in cable systems. The integration of these advanced monitoring and pattern recognition systems is expected to lead to significant improvements in the operational efficiency and safety of electrical networks.