Assessing the Feasibility of Bordered Block Diagonal Reordering in Power System Matrices using Fully Convolutional Network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In electromagnetic transient (EMT) simulations for power systems and inverter-based resources (IBRs), the arrangement of states within the system's linear equations, represented by matrix A in Ax=b, is critical. The state ordering in matrix A can highlight distinct characteristics of the system's graph, and identifying an optimal state ordering is crucial for efficient computation. The choice of state ordering, however, is dependent on the solver used, as each solver may perform optimally with different matrix patterns. With a wide array of matrix reordering algorithms available, selecting the most suitable one becomes challenging without insights into the matrix's ideal configuration. To address this, the paper proposes a fully convolutional network (FCN) to evaluate the reordering potential of the A matrix into a bordered block diagonal (BBD) pattern, which is commonly observed in power system and IBR modeling. The FCN's assessment aims to streamline the solver's operation, which in turn could substantially reduce the computational time required to find a solution.

Original languageEnglish
Title of host publication2024 IEEE Power and Energy Society General Meeting, PESGM 2024
PublisherIEEE Computer Society
ISBN (Electronic)9798350381832
DOIs
StatePublished - 2024
Event2024 IEEE Power and Energy Society General Meeting, PESGM 2024 - Seattle, United States
Duration: Jul 21 2024Jul 25 2024

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2024 IEEE Power and Energy Society General Meeting, PESGM 2024
Country/TerritoryUnited States
CitySeattle
Period07/21/2407/25/24

Funding

Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy. This material is based upon work supported by ORNL DRD program INTERSECT initiative program number 32112883. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

  • Bordered block diagonal matrix
  • Electrical magnetic transient simulation
  • Fully convolutional network

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