# Structure-preserving Model Reduction of Parametric Power Networks

arXiv: https://arxiv.org/abs/2102.05179 · HTML: https://mlsanity.com/paper/2102.05179/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2021.

## Built on by (most cited first, 1 of 1)

- [Learning Coherent Clusters in Weakly-Connected Network Systems](https://mlsanity.com/paper/2211.15301/) (2022)

## Abstract (arXiv)

We develop a structure-preserving parametric model reduction approach for linearized swing equations where parametrization corresponds to variations in operating conditions. We employ a global basis approach to develop the parametric reduced model in which we concatenate the local bases obtained via H2-based interpolatory model reduction. The residue of the underlying dynamics corresponding to the simple pole at zero varies with the parameters. Therefore, to have bounded H2 and H_∞ errors, the reduced model residue for the pole at zero should match the original one over the entire parameter domain. Our framework achieves this goal by enriching the global basis based on a residue analysis. The effectiveness of the proposed method is illustrated through two numerical examples.