A Novel Data Segmentation Method for Data-driven Phase Identification

TL;DR

This paper introduces a data segmentation and clustering approach for smart meter phase identification, improving accuracy and robustness in both known and unknown phase label scenarios through innovative data exclusion and similarity measures.

Contribution

It proposes a novel data segmentation method combined with hierarchical clustering and CTS for enhanced phase identification accuracy, especially when phase labels are unknown.

Findings

Outperforms existing methods in accuracy.

Demonstrates robustness on synthetic and real data.

Effective in both known and unknown phase label cases.

Abstract

This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase are weakened. Then, using the selected data segments, a hierarchical clustering method is used to calculate the correlation distances and cluster the smart meters. If the phase labels are unknown, a Connected-Triple-based Similarity (CTS) method is adapted to further improve the phase identification accuracy of the ensemble clustering method. The methods are developed and tested on both synthetic and real feeder data sets. Simulation results show that the proposed phase identification algorithm outperforms the state-of-the-art methods in both accuracy and robustness.