A Multi-Function AAA Algorithm Applied to Frequency-Dependent Line Modeling: Preprint

Kumaraguru Prabakar; Matthew Reynolds; Jonathan Maack; Lucas Monzon; William Johns

A Multi-Function AAA Algorithm Applied to Frequency-Dependent Line Modeling: Preprint

Kumaraguru Prabakar, Matthew Reynolds, Jonathan Maack, Lucas Monzon, William Johns

University of Colorado Boulder

Research output: Contribution to conference › Paper

Abstract

We present a new multi-input rational approximation algorithm (MIAAA) and illustrate its advantages respect to the popular Vector Fitting (VF) algorithm using examples of approximations of admittance matrices discussed in a very recent paper and by the creators of VF. We show that not only MIAAA outperforms VF in terms of achieving better accuracy using a smaller number of poles, but also has no numerical issues to achieve convergence. In contrast to VF, MIAA is not sensitive to the location of input sample points and it does not require good estimates for the location of the desired approximation poles.

Original language	American English
Number of pages	8
State	Published - 2020
Event	2020 IEEE Power and Energy Society General Meeting (IEEE PES GM) - Montreal, Canada Duration: 2 Aug 2020 → 6 Aug 2020

Conference

Conference	2020 IEEE Power and Energy Society General Meeting (IEEE PES GM)
City	Montreal, Canada
Period	2/08/20 → 6/08/20

Bibliographical note

See NREL/CP-5D00-79033 for paper as published in proceedings

NREL Publication Number

NREL/CP-5D00-75381

Keywords

AAA algorithm
barycentric formula
computational modeling
function approximation
power system modeling
rational approximations
vector fitting

Access to Document

https://www.nrel.gov/docs/fy20osti/75381.pdf

Cite this

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title = "A Multi-Function AAA Algorithm Applied to Frequency-Dependent Line Modeling: Preprint",

abstract = "We present a new multi-input rational approximation algorithm (MIAAA) and illustrate its advantages respect to the popular Vector Fitting (VF) algorithm using examples of approximations of admittance matrices discussed in a very recent paper and by the creators of VF. We show that not only MIAAA outperforms VF in terms of achieving better accuracy using a smaller number of poles, but also has no numerical issues to achieve convergence. In contrast to VF, MIAA is not sensitive to the location of input sample points and it does not require good estimates for the location of the desired approximation poles.",

keywords = "AAA algorithm, barycentric formula, computational modeling, function approximation, power system modeling, rational approximations, vector fitting",

author = "Kumaraguru Prabakar and Matthew Reynolds and Jonathan Maack and Lucas Monzon and William Johns",

note = "See NREL/CP-5D00-79033 for paper as published in proceedings; 2020 IEEE Power and Energy Society General Meeting (IEEE PES GM) ; Conference date: 02-08-2020 Through 06-08-2020",

year = "2020",

language = "American English",

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TY - CONF

T1 - A Multi-Function AAA Algorithm Applied to Frequency-Dependent Line Modeling: Preprint

AU - Prabakar, Kumaraguru

AU - Reynolds, Matthew

AU - Maack, Jonathan

AU - Monzon, Lucas

AU - Johns, William

N1 - See NREL/CP-5D00-79033 for paper as published in proceedings

PY - 2020

Y1 - 2020

N2 - We present a new multi-input rational approximation algorithm (MIAAA) and illustrate its advantages respect to the popular Vector Fitting (VF) algorithm using examples of approximations of admittance matrices discussed in a very recent paper and by the creators of VF. We show that not only MIAAA outperforms VF in terms of achieving better accuracy using a smaller number of poles, but also has no numerical issues to achieve convergence. In contrast to VF, MIAA is not sensitive to the location of input sample points and it does not require good estimates for the location of the desired approximation poles.

AB - We present a new multi-input rational approximation algorithm (MIAAA) and illustrate its advantages respect to the popular Vector Fitting (VF) algorithm using examples of approximations of admittance matrices discussed in a very recent paper and by the creators of VF. We show that not only MIAAA outperforms VF in terms of achieving better accuracy using a smaller number of poles, but also has no numerical issues to achieve convergence. In contrast to VF, MIAA is not sensitive to the location of input sample points and it does not require good estimates for the location of the desired approximation poles.

KW - AAA algorithm

KW - barycentric formula

KW - computational modeling

KW - function approximation

KW - power system modeling

KW - rational approximations

KW - vector fitting

M3 - Paper

T2 - 2020 IEEE Power and Energy Society General Meeting (IEEE PES GM)

Y2 - 2 August 2020 through 6 August 2020

ER -

A Multi-Function AAA Algorithm Applied to Frequency-Dependent Line Modeling: Preprint

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

Access to Document

Fingerprint

Cite this