Data-driven modeling of power networks<sup>1</sup>

Safaee, Bita; Gugercin, Serkan

doi:10.1109/cdc45484.2021.9683100

Cited by 1 publication

(2 citation statements)

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“…We test the proposed approach on two models: (i) the SM model of the 39-bus New England test system and (ii) IEEE 118 bus system, included in the MATPOWER software toolbox [44], [45]. We focus on a single-output system (p = 1) by choosing the arithmetic mean of all phase angles as the output as in [33], [34]. Models are generated by MATPOWER and MATLAB toolbox pg-sync-models [25] that provide all the necessary parameters to form (2) including J i , D i , ω R , K ij , γ ij and B i .…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The recent work [33] presents the idea of transforming the swing equations into a quadratic system, lifts nonlinear swing equations to a quadratic one, and employs quadratic balanced truncation model order reduction technique. A non-intrusive data-driven modeling framework for power network dynamics using Lift and Learn [31] has been studied in [34].…”

Section: Introductionmentioning

confidence: 99%

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Structure-Preserving Model Reduction for Nonlinear Power Grid Network

Safaee¹,

Gugercin²

2022

Preprint

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We develop a structure-preserving system-theoretic model reduction framework for nonlinear power grid networks. First, via a lifting transformation, we convert the original nonlinear system with trigonometric nonlinearities to an equivalent quadratic nonlinear model. This equivalent representation allows us to employ the H2-based model reduction approach, Quadratic Iterative Rational Krylov Algorithm (Q-IRKA), as an intermediate model reduction step. Exploiting the structure of the underlying power network model, we show that the model reduction bases resulting from Q-IRKA have a special subspace structure, which allows us to effectively construct the final model reduction basis. This final basis is applied on the original nonlinear structure to yield a reduced model that preserves the physically meaningful (second-order) structure of the original model. The effectiveness of our proposed framework is illustrated via two numerical examples.

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