Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Alvaro Bastos, Surya Santoso, Venkat Krishnan, Yingchen Zhang

Research output: NRELPresentation


Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.
Original languageAmerican English
Number of pages6
StatePublished - 2020

Publication series

NamePresented at the IEEE Power and Energy Society General Meeting (PES), 3-6 August 2020

Bibliographical note

See NREL/CP-5D00-75247 for related conference paper

NREL Publication Number

  • NREL/PR-5D00-77371


  • distributed generation
  • distribution system
  • ensemble regressor
  • machine learning
  • sensor allocation
  • voltage prediction


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