A Novel Power-Band based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification

TL;DR

This paper introduces a power-band-based data segmentation method to improve the accuracy of meter phase and transformer-meter pairing identification, especially under high load conditions, outperforming existing techniques.

Contribution

The novel PBDS method enhances correlation-based identification by selecting high-quality voltage segments, significantly reducing misidentification rates under challenging conditions.

Findings

Outperforms existing methods in accuracy and robustness

Validated with field verification on 13 utility feeders

Effective under high power consumption scenarios

Abstract

This paper presents a novel power-band-based data segmentation (PBDS) method to enhance the identification of meter phase and meter-transformer pairing. Meters that share the same transformer or are on the same phase typically exhibit strongly correlated voltage profiles. However, under high power consumption, there can be significant voltage drops along the line connecting a customer to the distribution transformer. These voltage drops significantly decrease the correlations among meters on the same phase or supplied by the same transformer, resulting in high misidentification rates. To address this issue, we propose using power bands to select highly correlated voltage segments for computing correlations, rather than relying solely on correlations computed from the entire voltage waveforms. The algorithm's performance is assessed by conducting tests using data gathered from 13 utility feeders. To ensure the credibility of the identification results, utility engineers conduct field verification for all 13 feeders. The verification results unequivocally demonstrate that the proposed algorithm surpasses existing methods in both accuracy and robustness.