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

Liang Zhang; Gang Wang; Georgios B. Giannakis

doi:10.1109/DSW.2019.8755581

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

Liang Zhang
, Gang Wang
, Georgios B. Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

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 language	English (US)
Title of host publication	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	258-262
Number of pages	5
ISBN (Electronic)	9781728107080
DOIs	https://doi.org/10.1109/DSW.2019.8755581
State	Published - Jun 2019
Event	2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duration: Jun 2 2019 → Jun 5 2019

Publication series

Name	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference	2019 IEEE Data Science Workshop, DSW 2019
Country/Territory	United States
City	Minneapolis
Period	6/2/19 → 6/5/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

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

Publisher link

10.1109/DSW.2019.8755581

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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). Article 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 proceeding › Conference 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

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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.",

keywords = "Distribution system state estimation, deep neural network, pseudo measurement, recurrent neural network",

author = "Liang Zhang and Gang Wang and Giannakis, \{Georgios B.\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Data Science Workshop, DSW 2019 ; Conference date: 02-06-2019 Through 05-06-2019",

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doi = "10.1109/DSW.2019.8755581",

language = "English (US)",

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

Abstract

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Keywords

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