Learning connectivity and higher-order interactions in radial distribution grids

Conference paper (2020)

Authors

Qiuling Yang Beijing Institute of Technology
Mario Coutino Signal Processing Systems - EEMCS
Gang Wang University of Minnesota
Georgios B. Giannakis University of Minnesota
G.J.T. Leus Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2020 Qiuling Yang, Mario Coutino, Gang Wang, Georgios B. Giannakis, G.J.T. Leus
DOI
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https://doi.org/10.1109/ICASSP40776.2020.9054665
Graph Distribution microgrid Higher-order interaction Topology identification Volterra model
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Published Date

2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

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.