Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks

Liang Zhang, Gang Wang, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Massive integration of renewables and electric vehicles comes with unknown dynamics - what exemplifies the need for fast, accurate, and robust distribution system state estimation (DSSE). Due to limited real-time measurements however, optimization-oriented DSSE faces major challenges related to convergence, as well as multiple global/local minima. To address these challenges, this paper puts forth a novel deep neural network (DNN)-based computational framework for DSSE that consists of two modules: a deep recurrent neural network (RNN) based pseudo-measurement postulating module, and a prox-linear net-based real-time state estimation module. Both RNN and prox-linear nets learn complex nonlinear functions, and can afford efficient training by leveraging existing deep learning platforms. Numerical tests with semi-real load data demonstrate the merits of the DNN-based DSSE approach.

Original languageEnglish (US)
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages258-262
Number of pages5
ISBN (Electronic)9781728107080
DOIs
StatePublished - Jun 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 5 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference2019 IEEE Data Science Workshop, DSW 2019
CountryUnited States
CityMinneapolis
Period6/2/196/5/19

Bibliographical note

Funding Information:
This work was supported in part by NSF grants 1508993, 1509040, and 1711471.

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Distribution system state estimation
  • deep neural network
  • pseudo measurement
  • recurrent neural network

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