On the Use of Smart Meter Data to Estimate the Voltage Magnitude on the Primary Side of Distribution Service Transformers

Marcos Netto, Jun Hao, Harsha Padullaparti, Venkat Krishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper develops a novel method to estimate the voltage magnitude on the primary side of distribution service transformers. The proposed method relies exclusively on smart meters, and therefore it is fully data-driven. This is an important feature because electric utilities have detailed models of only the primary network - that is, the network between the distribution substation and the primary side of service transformers that are installed closer to end-customer sites. The network that connects the secondary side of service transformers to end-customer sites, referred to as the secondary network, is simply represented by a lumped load. For each secondary network, the proposed method uses data acquired from only 2 smart meters: the closest and the farthest-in the sense of electrical distance - from the service transformer. As a reference to this feature, the proposed method is named SM2Vp. To our knowledge, this is the first time a method is shown to provide actionable information for realtime operation and control of power distribution grids using only two smart meters per secondary network. This is important because utilities have experienced barriers in managing and using large data sets for real-time operation and control. SM2Vp is primarily intended to provide pseudo-measurements for distribution system state estimation, but it can also be used directly for voltage control schemes. The performance of SM2Vp is demonstrated by numerical simulations carried out on three secondary network synthetic models and by using field data provided by a utility partner serving customers in southwestern California. A maximum relative error of approximately 3.9% or less is observed for the primary voltage magnitude estimates in all numerical experiments.

Original languageEnglish (US)
Title of host publication2021 IEEE Power and Energy Society General Meeting, PESGM 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665405072
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE Power and Energy Society General Meeting, PESGM 2021 - Washington, United States
Duration: Jul 26 2021Jul 29 2021

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2021-July
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Country/TerritoryUnited States
CityWashington
Period7/26/217/29/21

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

Keywords

  • distribution service transformer
  • Distribution system state estimation
  • pseudo-measurement
  • smart meter

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