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

6 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

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

Access

10.1109/DSW.2019.8755581

OpenUrl availability

Full text

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). 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

@inproceedings{a2df8a43064e4289bd2d1fe234d55952,

title = "Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks",

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 = "Funding Information: This work was supported in part by NSF grants 1508993, 1509040, and 1711471. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Data Science Workshop, DSW 2019 ; Conference date: 02-06-2019 Through 05-06-2019",

year = "2019",

month = jun,

doi = "10.1109/DSW.2019.8755581",

language = "English (US)",

series = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "258--262",

booktitle = "2019 IEEE Data Science Workshop, DSW 2019 - Proceedings",

}

TY - GEN

T1 - Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks

AU - Zhang, Liang

AU - Wang, Gang

AU - Giannakis, Georgios B.

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - Distribution system state estimation

KW - deep neural network

KW - pseudo measurement

KW - recurrent neural network

UR - http://www.scopus.com/inward/record.url?scp=85069447894&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069447894&partnerID=8YFLogxK

U2 - 10.1109/DSW.2019.8755581

DO - 10.1109/DSW.2019.8755581

M3 - Conference contribution

AN - SCOPUS:85069447894

T3 - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

SP - 258

EP - 262

BT - 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Data Science Workshop, DSW 2019

Y2 - 2 June 2019 through 5 June 2019

ER -

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this