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

Fingerprint

State estimation
Physics
Recurrent neural networks
Electric vehicles
Time measurement
Deep neural networks

Keywords

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

Cite this

Zhang, L., Wang, G., & Giannakis, G. B. (2019). Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks. In 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings (pp. 258-262). [8755581] (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2019.8755581

Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks. / Zhang, Liang; Wang, Gang; Giannakis, Georgios B.

2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 258-262 8755581 (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings).

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

Zhang, L, Wang, G & Giannakis, GB 2019, Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks. in 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings., 8755581, 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 258-262, 2019 IEEE Data Science Workshop, DSW 2019, Minneapolis, United States, 6/2/19. https://doi.org/10.1109/DSW.2019.8755581
Zhang L, Wang G, Giannakis GB. Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks. In 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 258-262. 8755581. (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings). https://doi.org/10.1109/DSW.2019.8755581
Zhang, Liang ; Wang, Gang ; Giannakis, Georgios B. / Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks. 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 258-262 (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings).
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