Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Research output: Contribution to conferencePaperpeer-review

3 Scopus Citations

Abstract

Many distribution network monitoring and control applications-including state estimation, Volt/V Ar optimization, and network reconfiguration-rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2022
Event2022 IEEE Power and Energy Society General Meeting, PESGM 2022 - Denver, United States
Duration: 17 Jul 202221 Jul 2022

Conference

Conference2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Country/TerritoryUnited States
CityDenver
Period17/07/2221/07/22

Bibliographical note

See NREL/CP-5D00-81336 for preprint

NREL Publication Number

  • NREL/CP-5D00-84976

Keywords

  • Advanced metering infrastructure (AMI)
  • distribution network
  • machine learning
  • phase identification
  • photovoltaic systems
  • smart meter
  • supervised learning

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