  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.
