US 11,860,210 B2
Electrical phase identification using a clustering algorithm
Yingjuan Du, San Diego, CA (US); and Brendan Stellarum, San Diego, CA (US)
Assigned to Itron, Inc., Liberty Lake, WA (US)
Filed by Itron, Inc., Liberty Lake, WA (US)
Filed on Apr. 28, 2022, as Appl. No. 17/732,505.
Claims priority of provisional application 63/246,269, filed on Sep. 20, 2021.
Prior Publication US 2023/0100242 A1, Mar. 30, 2023
Int. Cl. G01R 29/18 (2006.01); H02J 13/00 (2006.01); G01R 19/25 (2006.01); H02J 3/12 (2006.01)
CPC G01R 29/18 (2013.01) [G01R 19/2513 (2013.01); H02J 3/12 (2013.01); H02J 13/00034 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
calculating voltage correlations of meter-to-meter combinations of a plurality of electricity meters based on voltage time series data collected over a preselected collection time period, each electricity meter of the plurality of electricity meters connected to one of six phases comprising three line-to-neutral phases and three line-to-line phases;
clustering the plurality of electricity meters into three initial kernels representing the three line-to-neutral phases based on voltage correlations;
for each of the three initial kernels, calculating correlation values with each electricity meter of the plurality of electricity meters;
determining three new kernels based on the correlation values;
clustering the plurality of electricity meters into three groups based on a hybrid index for the three new kernels calculated based on average correlation values associated with each electricity meter;
forming six new kernels of electricity meters representing the six phases based on the average correlation values associated with each electricity meter; and
assigning a predicted phase to an electricity meter of the plurality of electricity meters based on corresponding correlation values of the electricity meter with each of the six new kernels based on the voltage time series data.