Low Voltage Customer Phase Identification Methods Based on Smart Meter Data

TL;DR

This paper compares existing voltage and power measurement-based methods for customer phase identification using smart meter data, and introduces a novel ensemble learning approach that enhances accuracy by combining data sources.

Contribution

The paper provides a comprehensive comparison of phase identification methods and proposes a new ensemble learning technique to improve accuracy using multi-source smart meter data.

Findings

Voltage data generally yields better phase identification accuracy than power data.

The novel ensemble method improves accuracy when combining voltage and power data.

Smart meter data accuracy and penetration levels significantly affect method performance.

Abstract

The increased deployment of distributed energy generation and the integration of new, large electric loads such as electric vehicles and heat pumps challenge the correct and reliable operation of low voltage distribution systems. To tackle potential problems, active management solutions are proposed in the literature, which require distribution system models that include the phase connectivity of all the consumers in the network. However, information on the phase connectivity is in practice often unavailable. In this work, several voltage and power measurement-based phase identification methods from the literature are implemented. A consistent comparison of the methods is made across different smart meter accuracy classes and smart meter penetration levels using publicly available data. Furthermore, a novel method is proposed that makes use of ensemble learning and that can combine data from different measurement campaigns. The results indicate that generally better results are obtained with voltage data compared to power data from smart meters of the same accuracy class. If power data is available too, the novel ensemble method can improve the accuracy of the phase identification obtained from voltage data alone.