Multi-Fidelity Gaussian Process for Distribution System Voltage Probabilistic Estimation with PVs

Jinxian Zhang, Junbo Zhao, Ketian Ye, Fei Ding

Research output: Contribution to conferencePaper

2 Scopus Citations

Abstract

The increasing penetration of behind-the-meter PVs causes challenges to maintain voltage security due to the lack of distribution system visibility. This paper proposes a nonlinear autoregressive Gaussian process (NARGP) approach to fuse limited number of SCADA/AMI data together with historical pseudo measurements for distribution node voltage probabilistic estimation. The high-fidelity SCADA data are fused with the low-fidelity AMI and pseudo measurements by the autoregressive algorithm embedded in the Gaussian process. This allows us to use multi-fidelity data to achieve entire distribution system voltage visibility. Numerical results carried out on the IEEE 123node system demonstrate that the NARGP method is able to obtain high accuracy in estimating bus voltage and quantifying estimation uncertainties as compared to other approaches.
Original languageAmerican English
Pages3088-3093
Number of pages6
DOIs
StatePublished - 2023
Event2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) - Chengdu, China
Duration: 11 Nov 202213 Nov 2022

Conference

Conference2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)
CityChengdu, China
Period11/11/2213/11/22

NREL Publication Number

  • NREL/CP-5D00-86597

Keywords

  • distribution system estimation
  • distribution system visibility
  • Gaussian process
  • renewable energy integration

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