Model Expansion to Realize Preferred Topologies

Lesieutre, Bernard C.; Singh, Manish K.

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https://hdl.handle.net/2142/130288 Copy

Description

Title

Model Expansion to Realize Preferred Topologies

Author(s)

• Lesieutre, Bernard C.
• Singh, Manish K.

Issue Date

2025-09-17

Keyword(s)

• Power systems
• Power flow
• Kron reduction

Abstract

Model reduction techniques are routinely used to focus on specific phenomena and features in a representation, typically by reducing the number of variables. In electric circuits where only a subset of nodes may be of interest, Kron reduction is routinely employed to eliminate the other nodes, while preserving the electrical behavior as seen from the retained nodes. While resulting in networks with fewer nodes, Kron reduction has potential drawbacks. By design, the original network’s structure is not preserved. Therefore, despite the terminal behavior being equivalent to that of the originating system, practical algorithms may find the ensuing analysis more computationally demanding. For instance, Kron reduction of radial networks yields meshed topologies. Several (optimal) power flow algorithms provide stronger performance guarantees for radial networks. In these cases, having a radial network with a higher number of nodes may be a pertinent choice. In this paper, we pursue a course akin to reverse Kron reduction in which we purposely enlarge a network model to achieve a topology that may have favorable characteristics for downstream analysis. This larger model will preserve the electrical behavior as seen from the original nodes and will revert to the original network when Kron reduction is employed. We consider the question of when it may be possible to convert a meshed network to a larger tree network, and we examine conversion to other structures that may have favorable topologies.

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2025 61st Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

Conference Paper/Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130288&&

Copyright and License Information

Copyright 2025 is held by Bernard C. Lesieutre and Manish K. Singh.

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