Phase Identification of Smart Meters Using a Fourier Series Compression and a Statistical Clustering Algorithm

TL;DR

This paper introduces a novel method combining Fourier series compression and statistical clustering to accurately identify phase connectivity of smart meters in electrical distribution systems, improving maintenance and operational efficiency.

Contribution

It presents a new hierarchical clustering approach using Fourier-transformed voltage data to determine phase membership, validated over multiple periods and feeders.

Findings

Clusters remain consistent over two months

Meters on the same feeder are correctly grouped

Fourier coefficient-based clustering effectively identifies phases

Abstract

Accurate labeling of phase connectivity in electrical distribution systems is important for maintenance and operations but is often erroneous or missing. In this paper, we present a process to identify which smart meters must be in the same phase using a hierarchical clustering method on voltage time series data. Instead of working with the time series data directly, we apply the Fourier transform to represent the data in their frequency domain, remove $98\%$ of the Fourier coefficients, and use the remaining coefficients to cluster the meters are in the same phase. Result of this process is validated by confirming that cluster (phase) membership of meters does not change over two monthly periods. In addition, we also confirm that meters that belong to the same feeder within the distribution network are correctly classified into the same cluster, that is, assigned to the same phase.