A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements

Jiyu Wang, Harsha Padullaparti, Santosh Veda, Murali Baggu, Martha Symko-Davies, Amin Salmani, Tom Bialek

Research output: Contribution to conferencePaperpeer-review

2 Scopus Citations

Abstract

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops a machine learning based approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurement. The machine learning model is developed by using random forest algorithm. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas Electric Company.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2021
Event2021 IEEE Power and Energy Society General Meeting, PESGM 2021 - Washington, United States
Duration: 26 Jul 202129 Jul 2021

Conference

Conference2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Country/TerritoryUnited States
CityWashington
Period26/07/2129/07/21

Bibliographical note

See NREL/CP-5D00-78299 for preprint

NREL Publication Number

  • NREL/CP-5D00-82306

Keywords

  • Advanced metering infrastructure (AMI)
  • distribution system
  • service transformer
  • smart grid
  • voltage estimation

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