Topology Identification of Power Distribution Systems Using Time Series of Voltage Measurements

Cody Francis, Vittal Rao, Rodrigo D. Trevizan, Matthew J. Reno

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Topology identification in transmission systems has historically been accomplished using SCADA measurements. In distribution systems, however, SCADA measurements are insufficient to determine system topology. An accurate system topology is essential for distribution system monitoring and operation. Recently there has been a proliferation of Advanced Metering Infrastructure (AMI) by the electrical utilities, which improved the visibility into distribution systems. These measurements offer a unique capability for Distribution System Topology Identification (DSTI). A novel approach to DSTI is presented in this paper which utilizes the voltage magnitudes collected by distribution grid sensors to facilitate identification of the topology of the distribution network in real-time using Linear Discriminant Analysis (LDA) and Regularized Diagonal Quadratic Discriminant Analysis (RDQDA). The results show that this method can leverage noisy voltage magnitude readings from load buses to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.

Original languageEnglish
Title of host publication2021 IEEE Power and Energy Conference at Illinois, PECI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728186481
DOIs
StatePublished - Apr 1 2021
Event2021 IEEE Power and Energy Conference at Illinois, PECI 2021 - Urbana, United States
Duration: Apr 1 2021Apr 2 2021

Publication series

Name2021 IEEE Power and Energy Conference at Illinois, PECI 2021

Conference

Conference2021 IEEE Power and Energy Conference at Illinois, PECI 2021
Country/TerritoryUnited States
CityUrbana
Period04/1/2104/2/21

Keywords

  • Advanced metering infrastructure
  • distribution system topology identification
  • linear discriminant analysis
  • smart grids

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