An optimal modal approximation method for model reduction of linear power system models

Ríos, Alejandra; Messina, A.R.

doi:10.1016/j.ijepes.2012.08.001

Cited by 10 publications

(3 citation statements)

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“…Also, the methodology should allow to identify dominant poles of the system to guarantee its fidelity from the controllability/observability point of view [6]. This corresponds to what is known in the literature as modal truncation or modal approximation [7]- [10]. Modal approximation basically decomposes a transfer function into a set of partial fractions retaining only those having their poles closest to the imaginary axis [8].…”

Section: Introductionmentioning

confidence: 99%

Dominant Modes Identification of Linear Systems via Geometrical Search

Semlyen

Ramirez

Gustavsen

et al. 2021

IEEE Trans. Power Delivery

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This paper presents a novel approach, based on the theory of hyperplanes, for mode identification of linear systems. The proposed approach can operate on either a set of ordinary differential equations (converted to diagonal form, if needed) or a set of partial fractions derived from a synthesized transfer function of the system under analysis. For either format, the linear system is structured to have as unknown variable a vector containing the residues. Singular value decomposition is initially used to identify an initial sparsity of the residue vector where the number of nonzero values corresponds to the pre-defined order of the dominant poles (eigenvalues) under search. An algorithm based on geometrical search of hyperplanes is used to optimize the selection of the nonzero residue locations, minimizing the residual of the zero residue hyperplanes. Finally, a recalculation of the residues is carried out by using the obtained optimal sparsity.

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Section: Introductionmentioning

confidence: 99%

Dominant Modes Identification of Linear Systems via Geometrical Search

Semlyen

Ramirez

Gustavsen

et al. 2021

IEEE Trans. Power Delivery

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show abstract

“…Unlike the other existing methods, it preserves the physical structure while keeping some variables of the system, which makes the simulation and analysis more flexible: only a few things have to be changed before each new simulation. This method uses modal analysis [17] tools, such as the participation factors [18], to analyze the poles of the system and derive reduced models.…”

Section: Introductionmentioning

confidence: 99%

Model reduction of converters for the analysis of 100% power electronics transmission systems

Cossart

Colas

Kestelyn

2018

2018 IEEE International Conference on Industrial Technology (ICIT)

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This is an author-deposited version published in: https://sam.ensam.eu Handle ID: .http://hdl.handle.net/10985/14536 To cite this version :Quentin COSSART, Frédéric COLAS, Xavier KESTELYN -Model reduction of converters for the analysis of 100% power electronics transmission systems -Abstract-Because of the fast development of wind and solar photovoltaic power plants, as well as the multiplication of high voltage direct current links, the analysis and simulation of large transmission systems with a high penetration of power electronic converters have become necessary. In this situation, reduced models of converters are needed to make the simulation faster and the stability analysis more straightforward. These reduced models need to be accurate enough, depending on the needed accuracy, and they have to keep the physical structure of the converter full model to be easily implemented on classical analysis software. A structure-preserving method to reduce the state order of a grid forming converter model is presented here. It is based on a modal analysis to remove the fastest poles of the system while keeping the slowest almost unchanged.

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“…of power system component modeling and control, a kind of component structural models with DA subsystem form proposed [11,12], and discusses the decentralized nonlinear controller design method for models with this form [13,14]. The Ref.…”

Section: Introductionmentioning

confidence: 99%