Data-Driven Power System Linear Model Identification via Quadrature-Based Balanced Truncation

Authors

Keywords:

Balanced truncation, quadrature rules, system identification, directional frequency data-set, power system linear model

Abstract

This paper introduces the novel formulation of the quadrature-based balanced truncation (QBBT) method to precisely identify large-scale power system linear models. The QBBT interpolates a directional frequency dataset that is extracted from a dynamic system, following a quadrature rule to be applied from weighted data to derive a linear system model based on approximations of Gramians, and providing an alternative approach to the classic balanced truncation (BT) formulation that makes use of the system model. In this investigation, an accurate theoretical framework is presented to achieve the application of QBBT considering power systems as black-box models. The Boyd/Clenshaw-Curtis (BCC) quadrature rule is implemented by using different adjustments to accurately identify linear models of different order. The attained results and their validation with analytical power system models confirm the method's potential to retail oscillatory dynamics. The QBBT's effectiveness is compared with the Loewner-based frequency interpolation (LBFI) approach and the BT method on the Klein-Rogers-Kundur (KRK) benchmark power system and the equivalent of the New England transmission system - New York power system (NETS-NYPS), achieving a small absolute error in the magnitude of the frequency response of the models of the order of 1x10-3 dB in comparison with other state-of-the-art methods.

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Author Biographies

Mario R. Arrieta Paternina, Universidad Nacional Autónoma de México (UNAM)

Mario R. Arrieta Paternina (Member IEEE) holds a B.Eng. and M.Eng. in Electrical Engineering from National University of Colombia, Medellin, Colombia, in 2007 and 2009, respectively. In 2017, he obtained his D.Sc. degree in Electrical Engineering from CINVESTAV, and he joined the Department of Electrical Engineering at the UNAM.

Jose A. Moreno Corbea, Universidad Nacional Autónoma de México (UNAM)

José A. Moreno Corbea (Student member IEEE) received the B.S. degree in electrical engineering from the Central University Marta Abreu of ``Las Villas'', Cuba, in 2017. He received the M.Sc. degree in electrical engineering from the UNAM in 2023. He is currently working toward the Ph.D. degree.

Juan M. Ramirez-Arredondo, Centro de Investigación y de Estudios Avanzados del IPN, Unidad Guadalajara

Juan M. Ramirez-Arredondo (Member, IEEE) received the Ph.D. degree in electrical engineering from UANL-Mexico, San Nicolás de los Garza, Mexico, in 1992. He joined the Department of Electrical Engineering, CINVESTAV Guadalajara, Mexico, in 1999, where he is currently a full-time Professor. His research interests include smart grids, microgrids, and power electronics applications. Dr. Ramirez is a member of the Mexican Research System.

Joe H. Chow, Rensselaer Polytechnic Institute

Joe H. Chow (F'92) received his MS and PhD degrees from the University of Illinois, Urbana-Champaign. After working in the General Electric Power System business in Schenectady, he joined Rensselaer Polytechnic Institute in 1987. He is an Institute Professor Emeritus and Senior Research Scientist at the Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA. His research interests include multivariable control, power system dynamics and control, voltage-sourced converter-based FACTS Controllers, and synchronized phasor data.

Alejandro Zamora-Mendez, Universidad Michoacana San Nicolás de Hidalgo

Alejandro Zamora-Mendez (M' 11) obtained his B.S. and M.Sc. in Electrical Engineering from Universidad Michoacana de San Nicolas de Hidalgo (UMSNH), Morelia, Mexico, in 2005 and 2008, respectively. He joined the Electrical Engineering Faculty, UMSNH in 2008. He received a D.Sc. degree in Electrical Engineering from CINVESTAV-Guadalajara in 2016.

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Published

2025-08-04

How to Cite

Arrieta Paternina, M. R., Moreno Corbea, J. A. ., Ramirez-Arredondo, J. M. ., Chow, J. H., & Zamora-Mendez, A. (2025). Data-Driven Power System Linear Model Identification via Quadrature-Based Balanced Truncation. IEEE Latin America Transactions, 23(9), 787–798. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/9597

Issue

Vol. 23 No. 9 (2025): Ordinary Issue

Section

Electric Energy