Latent Neural ODE for Integrating Multi-Timescale Measurements in Smart Distribution Grids

Shweta Dahale, Sai Munikoti, Balasubramaniam Natarajan, Rui Yang

Research output: Contribution to conferencePaperpeer-review

Abstract

Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness. The measurements from these sensors can be leveraged for real-time monitoring, control, and protection. However, these measurements are typically irregularly sampled. These measure-ments may also be intermittent due to communication bandwidth limitations. To tackle this problem, this paper proposes a novel latent neural ordinary differential equations (LODE) approach to aggregate the unevenly sampled multivariate time-series measurements. The proposed approach is flexible in performing both imputations and predictions while being computationally efficient. Simulation results on IEEE 37 bus test systems illustrate the efficiency of the proposed approach.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2023
Event2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023 - Washington, United States
Duration: 16 Jan 202319 Jan 2023

Conference

Conference2023 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2023
Country/TerritoryUnited States
CityWashington
Period16/01/2319/01/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

NREL Publication Number

  • NREL/CP-5D00-86284

Keywords

  • Multi time-scale measurements
  • Neural ordinary differential equations
  • Smart distribution system
  • variational autoencoder

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