On-line System Identification of Power System Linear Models

J. A. Moreno-Corbea; M. R.A. Paternina; D. Rodales; R. Reyes; F. Zelaya; A. Zamora; C. Toledo; C. Castrillón; A. Sánchez

doi:10.1109/PESGM52003.2023.10252494

On-line System Identification of Power System Linear Models

J. A. Moreno-Corbea^*
, M. R.A. Paternina^*
, D. Rodales
, R. Reyes
, F. Zelaya
, A. Zamora
, C. Toledo
, C. Castrillón
, A. Sánchez

^*Corresponding author for this work

Universidad Nacional Autónoma de México
Universidad Michoacana de San Nicolas de Hidalgo
U. Nuevo León
Utk
Universidad Industrial de Santander
Universidad Nacional de Colombia Medellin
Cinvestav

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper deals with the provision of adequate testing simulation environments to facilitate the evaluation of new system identification techniques to enhance power system stability. It develops and implements a testing architecture for online system identification approaches, taking advantage of the use of two well-known computational programs (MatlabTM and DIgSILENT PowerFactoryTM). To ensure a reliable and optimal system identification, this platform exploits a mutually beneficial relationship among four well-established mathematical techniques (discrete Fourier transform, Teager-Kaiser energy operator, the fast Fourier transform, and eigensystem realization algorithm). Their symbioses can capture the synchrophasor information, detect the right disturbance instant, optimally extract the dominant frequency, and properly identify the Markov parameters that assemble the descriptor form of Multiple-Input and Multiple-Output (MIMO) linear systems for modeling modern power grids. Numerical results unveil the potential for recreating the real behavior of power systems; in particular, the proposed architecture can deal with any system identification technique to be tested.

Original language	English
Title of host publication	2023 IEEE Power and Energy Society General Meeting, PESGM 2023
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665464413
DOIs	https://doi.org/10.1109/PESGM52003.2023.10252494
State	Published - 16 Jul 2023
Externally published	Yes
Event	2023 IEEE Power and Energy Society General Meeting, PESGM 2023 - Orlando, United States Duration: 16 Jul 2023 → 20 Jul 2023

Publication series

Name	2023 IEEE Power & Energy Society General Meeting (PESGM)

Conference

Conference	2023 IEEE Power and Energy Society General Meeting, PESGM 2023
Country/Territory	United States
City	Orlando
Period	16/07/23 → 20/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Online system identification
Teager-Kaiser energy operator
disturbance detection instant
eigensystem realization algorithm
frequency computation
phasor estimation

Access to Document

10.1109/PESGM52003.2023.10252494

Cite this

Moreno-Corbea, J. A., Paternina, M. R. A., Rodales, D., Reyes, R., Zelaya, F., Zamora, A., Toledo, C., Castrillón, C., & Sánchez, A. (2023). On-line System Identification of Power System Linear Models. In 2023 IEEE Power and Energy Society General Meeting, PESGM 2023 (2023 IEEE Power & Energy Society General Meeting (PESGM)). IEEE Computer Society. https://doi.org/10.1109/PESGM52003.2023.10252494

@inproceedings{11fe9a219b6345fdbb7c494a85441bec,

title = "On-line System Identification of Power System Linear Models",

abstract = "This paper deals with the provision of adequate testing simulation environments to facilitate the evaluation of new system identification techniques to enhance power system stability. It develops and implements a testing architecture for online system identification approaches, taking advantage of the use of two well-known computational programs (MatlabTM and DIgSILENT PowerFactoryTM). To ensure a reliable and optimal system identification, this platform exploits a mutually beneficial relationship among four well-established mathematical techniques (discrete Fourier transform, Teager-Kaiser energy operator, the fast Fourier transform, and eigensystem realization algorithm). Their symbioses can capture the synchrophasor information, detect the right disturbance instant, optimally extract the dominant frequency, and properly identify the Markov parameters that assemble the descriptor form of Multiple-Input and Multiple-Output (MIMO) linear systems for modeling modern power grids. Numerical results unveil the potential for recreating the real behavior of power systems; in particular, the proposed architecture can deal with any system identification technique to be tested.",

keywords = "Online system identification, Teager-Kaiser energy operator, disturbance detection instant, eigensystem realization algorithm, frequency computation, phasor estimation",

author = "Moreno-Corbea, \{J. A.\} and Paternina, \{M. R.A.\} and D. Rodales and R. Reyes and F. Zelaya and A. Zamora and C. Toledo and C. Castrill{\'o}n and A. S{\'a}nchez",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Power and Energy Society General Meeting, PESGM 2023 ; Conference date: 16-07-2023 Through 20-07-2023",

year = "2023",

month = jul,

day = "16",

doi = "10.1109/PESGM52003.2023.10252494",

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series = "2023 IEEE Power \& Energy Society General Meeting (PESGM)",

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}

Moreno-Corbea, JA, Paternina, MRA, Rodales, D, Reyes, R, Zelaya, F, Zamora, A, Toledo, C, Castrillón, C & Sánchez, A 2023, On-line System Identification of Power System Linear Models. in 2023 IEEE Power and Energy Society General Meeting, PESGM 2023. 2023 IEEE Power & Energy Society General Meeting (PESGM), IEEE Computer Society, 2023 IEEE Power and Energy Society General Meeting, PESGM 2023, Orlando, United States, 16/07/23. https://doi.org/10.1109/PESGM52003.2023.10252494

On-line System Identification of Power System Linear Models. / Moreno-Corbea, J. A.; Paternina, M. R.A.; Rodales, D. et al.
2023 IEEE Power and Energy Society General Meeting, PESGM 2023. IEEE Computer Society, 2023. (2023 IEEE Power & Energy Society General Meeting (PESGM)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On-line System Identification of Power System Linear Models

AU - Moreno-Corbea, J. A.

AU - Paternina, M. R.A.

AU - Rodales, D.

AU - Reyes, R.

AU - Zelaya, F.

AU - Zamora, A.

AU - Toledo, C.

AU - Castrillón, C.

AU - Sánchez, A.

PY - 2023/7/16

Y1 - 2023/7/16

N2 - This paper deals with the provision of adequate testing simulation environments to facilitate the evaluation of new system identification techniques to enhance power system stability. It develops and implements a testing architecture for online system identification approaches, taking advantage of the use of two well-known computational programs (MatlabTM and DIgSILENT PowerFactoryTM). To ensure a reliable and optimal system identification, this platform exploits a mutually beneficial relationship among four well-established mathematical techniques (discrete Fourier transform, Teager-Kaiser energy operator, the fast Fourier transform, and eigensystem realization algorithm). Their symbioses can capture the synchrophasor information, detect the right disturbance instant, optimally extract the dominant frequency, and properly identify the Markov parameters that assemble the descriptor form of Multiple-Input and Multiple-Output (MIMO) linear systems for modeling modern power grids. Numerical results unveil the potential for recreating the real behavior of power systems; in particular, the proposed architecture can deal with any system identification technique to be tested.

AB - This paper deals with the provision of adequate testing simulation environments to facilitate the evaluation of new system identification techniques to enhance power system stability. It develops and implements a testing architecture for online system identification approaches, taking advantage of the use of two well-known computational programs (MatlabTM and DIgSILENT PowerFactoryTM). To ensure a reliable and optimal system identification, this platform exploits a mutually beneficial relationship among four well-established mathematical techniques (discrete Fourier transform, Teager-Kaiser energy operator, the fast Fourier transform, and eigensystem realization algorithm). Their symbioses can capture the synchrophasor information, detect the right disturbance instant, optimally extract the dominant frequency, and properly identify the Markov parameters that assemble the descriptor form of Multiple-Input and Multiple-Output (MIMO) linear systems for modeling modern power grids. Numerical results unveil the potential for recreating the real behavior of power systems; in particular, the proposed architecture can deal with any system identification technique to be tested.

KW - Online system identification

KW - Teager-Kaiser energy operator

KW - disturbance detection instant

KW - eigensystem realization algorithm

KW - frequency computation

KW - phasor estimation

UR - https://www.scopus.com/pages/publications/85174690015

U2 - 10.1109/PESGM52003.2023.10252494

DO - 10.1109/PESGM52003.2023.10252494

M3 - Contribución a la conferencia

AN - SCOPUS:85174690015

T3 - 2023 IEEE Power & Energy Society General Meeting (PESGM)

BT - 2023 IEEE Power and Energy Society General Meeting, PESGM 2023

PB - IEEE Computer Society

T2 - 2023 IEEE Power and Energy Society General Meeting, PESGM 2023

Y2 - 16 July 2023 through 20 July 2023

ER -

On-line System Identification of Power System Linear Models

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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