Comparative Analysis of Segmented Correlation Trimmed Mean Algorithm for Locating Random and Static Partial Discharges in Power Cables

Abstract

Power cable monitoring for partial discharge (PD) source is a crucial act to identify the cable’s insulation weakness before the cable breakdown. Recently segmented correlation trimmed mean (SCTM) algorithm had been applied to double-end PD measurement method. The algorithm showed significant improvement in performance when applied to PD source localization on power cables. However, the previous research study only focuses on the performance of the SCTM algorithm for static PD localization. This paper employs a random PD model to evaluate the accuracy of the SCTM algorithm in detecting PD sources. MATLAB simulations compared SCTM algorithm's performance for random PD generation and static PD sources in double-end PD measurements. Results showed signal-to-noise (SNR) significantly influenced localization accuracy. Maximum PD estimation error ranged from 0.0539 to 0.0891 for random PD scenarios, while for static PD, it remained consistently at 0.0102 across all SNRs. The average PD estimation error was consistently lower for SCTM with static PD locations. As SNR improved, average errors converged to 0.0102 for both scenarios, indicating increased accuracy with lower noise levels.  In conclusion, the SCTM algorithm is more effective when used with static PD locations for power cable monitoring, leading to more accurate PD estimations. This research enhances the reliability and efficiency of PD source localization, vital for preserving power cable integrity and preventing breakdowns.