Topology Identification with Smart Meter Data Using Security Aware Machine Learning

Cody Francis, Vittal S. Rao, Rodrigo D. Trevizan

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

Abstract

Distribution system topology identification has historically been accomplished by unencrypting the information that is received from the smart meters and then running a topology identification algorithm. Unencrypted smart meter data introduces privacy and security issues for utility companies and their customers. This paper introduces security aware machine learning algorithms to alleviate the privacy and security issues raised with un-encrypted smart meter data. The security aware machine learning algorithms use the information received from the Advanced Metering Infrastructure (AMI) and identifies the distribution systems topology without unencrypting the AMI data by using fully homomorphic NTRU and CKKS encryption. The encrypted smart meter data is then used by Linear Discriminant Analysis, Convolution Neural Network, and Support Vector Machine algorithms to predict the distribution systems real time topology. This method can leverage noisy voltage magnitude readings from smart meters to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.

Original languageEnglish
Title of host publication2021 North American Power Symposium, NAPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665420815
DOIs
StatePublished - 2021
Event2021 North American Power Symposium, NAPS 2021 - College Station, United States
Duration: Nov 14 2021Nov 16 2021

Publication series

Name2021 North American Power Symposium, NAPS 2021

Conference

Conference2021 North American Power Symposium, NAPS 2021
Country/TerritoryUnited States
CityCollege Station
Period11/14/2111/16/21

Keywords

  • Advanced metering infrastructure
  • distribution system topology identification
  • linear discriminant analysis
  • security aware

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