Phase identification using smart meter data

Abstract

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Estimation of peak currents in LV feeders requires accurate data identifying customer service connections and the phases of each single-phase customer. The SMITN project applied correlation and machine-learning clustering techniques using smart meter voltage data to determine connection phases. Results show the impact of time-resolution and measurement duration on the accuracy of phase detections, either with or without substation monitoring, and highlight real-world improvements to smart meter voltage recording that would improve the performance of network monitoring.