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 language | English |
---|---|
Title of host publication | 2024 IEEE Power and Energy Society General Meeting, PESGM 2024 |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9798350381832 |
DOIs | |
State | Published - 2024 |
Event | 2024 IEEE Power and Energy Society General Meeting, PESGM 2024 - Seattle, United States Duration: Jul 21 2024 → Jul 25 2024 |
Publication series
Name | IEEE Power and Energy Society General Meeting |
---|---|
ISSN (Print) | 1944-9925 |
ISSN (Electronic) | 1944-9933 |
Conference
Conference | 2024 IEEE Power and Energy Society General Meeting, PESGM 2024 |
---|---|
Country/Territory | United States |
City | Seattle |
Period | 07/21/24 → 07/25/24 |
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