Learning connectivity and higher-order interactions in radial distribution grids

Qiuling Yang; Mario Coutino; Gang Wang; Georgios B. Giannakis; Geert Leus

doi:10.1109/ICASSP40776.2020.9054665

Learning connectivity and higher-order interactions in radial distribution grids

Qiuling Yang, Mario Coutino, Gang Wang, Georgios B. Giannakis, Geert Leus

Electrical and Computer Engineering

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

7 Scopus citations

Abstract

To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactions in recent literature, has not yet been addressed. The system operator can benefit from having this knowledge when re-configuring the grid in real time, to minimize power losses, balance loads, alleviate faults, or for scheduled maintenance. Establishing a connection between the celebrated exact distribution flow equations and the so-called self-driven graph Volterra model, this paper puts forth a nonlinear topology identification algorithm, that is able to reveal both the edge connections as well as their higher-order interactions. Preliminary numerical tests using real data on a 47-bus distribution grid showcase the merits of the proposed scheme relative to existing alternatives.

Original language	English (US)
Title of host publication	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5555-5559
Number of pages	5
ISBN (Electronic)	9781509066315
DOIs	https://doi.org/10.1109/ICASSP40776.2020.9054665
State	Published - May 2020
Event	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain Duration: May 4 2020 → May 8 2020

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2020-May
ISSN (Print)	1520-6149

Conference

Conference	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/Territory	Spain
City	Barcelona
Period	5/4/20 → 5/8/20

Bibliographical note

Funding Information:
Q. Yang was supported by the China Scholarship Council. M. Coutino and G. Leus were supported in part by the ASPIRE project 14926 (within the STW OTP program) financed by the Netherlands Organization for Scientific Research (NWO). M. Coutino was also supported by CONA-CYT. G. Wang and G. B. Giannakis were supported in part by NSF Grants 1509040, 1711471, and 1901134. E-mail: {yang6726, gang-wang, georgios}@umn.edu; {m.a.coutinominguez, g.j.t.leus}@tudelft.nl.

Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Keywords

Distribution microgrid
Graph
Higher-order interaction
Topology identification
Volterra model

Publisher link

10.1109/ICASSP40776.2020.9054665

Find It

Find It at U of M Libraries

Cite this

Yang, Q., Coutino, M., Wang, G., Giannakis, G. B., & Leus, G. (2020). Learning connectivity and higher-order interactions in radial distribution grids. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 5555-5559). [9054665] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054665

Learning connectivity and higher-order interactions in radial distribution grids. / Yang, Qiuling; Coutino, Mario; Wang, Gang et al.
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 5555-5559 9054665 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May).

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

Yang, Q, Coutino, M, Wang, G, Giannakis, GB & Leus, G 2020, Learning connectivity and higher-order interactions in radial distribution grids. in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings., 9054665, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, Institute of Electrical and Electronics Engineers Inc., pp. 5555-5559, 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, Barcelona, Spain, 5/4/20. https://doi.org/10.1109/ICASSP40776.2020.9054665

Yang Q, Coutino M, Wang G, Giannakis GB, Leus G. Learning connectivity and higher-order interactions in radial distribution grids. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 5555-5559. 9054665. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP40776.2020.9054665

Yang, Qiuling ; Coutino, Mario ; Wang, Gang et al. / Learning connectivity and higher-order interactions in radial distribution grids. 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 5555-5559 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{e512ceb0bc504a49907433c438a67610,

title = "Learning connectivity and higher-order interactions in radial distribution grids",

abstract = "To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactions in recent literature, has not yet been addressed. The system operator can benefit from having this knowledge when re-configuring the grid in real time, to minimize power losses, balance loads, alleviate faults, or for scheduled maintenance. Establishing a connection between the celebrated exact distribution flow equations and the so-called self-driven graph Volterra model, this paper puts forth a nonlinear topology identification algorithm, that is able to reveal both the edge connections as well as their higher-order interactions. Preliminary numerical tests using real data on a 47-bus distribution grid showcase the merits of the proposed scheme relative to existing alternatives.",

keywords = "Distribution microgrid, Graph, Higher-order interaction, Topology identification, Volterra model",

author = "Qiuling Yang and Mario Coutino and Gang Wang and Giannakis, {Georgios B.} and Geert Leus",

note = "Funding Information: Q. Yang was supported by the China Scholarship Council. M. Coutino and G. Leus were supported in part by the ASPIRE project 14926 (within the STW OTP program) financed by the Netherlands Organization for Scientific Research (NWO). M. Coutino was also supported by CONA-CYT. G. Wang and G. B. Giannakis were supported in part by NSF Grants 1509040, 1711471, and 1901134. E-mail: {yang6726, gang-wang, georgios}@umn.edu; {m.a.coutinominguez, g.j.t.leus}@tudelft.nl. Publisher Copyright: {\textcopyright} 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.; 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",

year = "2020",

month = may,

doi = "10.1109/ICASSP40776.2020.9054665",

language = "English (US)",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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

pages = "5555--5559",

booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",

}

TY - GEN

T1 - Learning connectivity and higher-order interactions in radial distribution grids

AU - Yang, Qiuling

AU - Coutino, Mario

AU - Wang, Gang

AU - Giannakis, Georgios B.

AU - Leus, Geert

N1 - Funding Information: Q. Yang was supported by the China Scholarship Council. M. Coutino and G. Leus were supported in part by the ASPIRE project 14926 (within the STW OTP program) financed by the Netherlands Organization for Scientific Research (NWO). M. Coutino was also supported by CONA-CYT. G. Wang and G. B. Giannakis were supported in part by NSF Grants 1509040, 1711471, and 1901134. E-mail: {yang6726, gang-wang, georgios}@umn.edu; {m.a.coutinominguez, g.j.t.leus}@tudelft.nl. Publisher Copyright: © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

PY - 2020/5

Y1 - 2020/5

N2 - To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactions in recent literature, has not yet been addressed. The system operator can benefit from having this knowledge when re-configuring the grid in real time, to minimize power losses, balance loads, alleviate faults, or for scheduled maintenance. Establishing a connection between the celebrated exact distribution flow equations and the so-called self-driven graph Volterra model, this paper puts forth a nonlinear topology identification algorithm, that is able to reveal both the edge connections as well as their higher-order interactions. Preliminary numerical tests using real data on a 47-bus distribution grid showcase the merits of the proposed scheme relative to existing alternatives.

AB - To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactions in recent literature, has not yet been addressed. The system operator can benefit from having this knowledge when re-configuring the grid in real time, to minimize power losses, balance loads, alleviate faults, or for scheduled maintenance. Establishing a connection between the celebrated exact distribution flow equations and the so-called self-driven graph Volterra model, this paper puts forth a nonlinear topology identification algorithm, that is able to reveal both the edge connections as well as their higher-order interactions. Preliminary numerical tests using real data on a 47-bus distribution grid showcase the merits of the proposed scheme relative to existing alternatives.

KW - Distribution microgrid

KW - Graph

KW - Higher-order interaction

KW - Topology identification

KW - Volterra model

UR - http://www.scopus.com/inward/record.url?scp=85091274198&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85091274198&partnerID=8YFLogxK

U2 - 10.1109/ICASSP40776.2020.9054665

DO - 10.1109/ICASSP40776.2020.9054665

M3 - Conference contribution

AN - SCOPUS:85091274198

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 5555

EP - 5559

BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020

Y2 - 4 May 2020 through 8 May 2020

ER -

Learning connectivity and higher-order interactions in radial distribution grids

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this