Data-Driven Modeling of Grid-Following Control in Grid-Connected Converters

Amir Bahador Javadi; Philip Pong

doi:10.1109/TPEC67884.2026.11513098

Data-Driven Modeling of Grid-Following Control in Grid-Connected Converters

Amir Bahador Javadi
, Philip Pong

Electrical and Computer Engineering

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

Abstract

As power systems evolve with the integration of renewable energy sources and the implementation of smart grid technologies, there is an increasing need for flexible and scalable modeling approaches capable of accurately capturing the complex dynamics of modern grids. To meet this need, various methods, such as the sparse identification of nonlinear dynamics and deep symbolic regression, have been developed to identify dynamical systems directly from data. In this study, we examine the application of a converter-based resource as a replacement for a traditional generator within a lossless transmission line linked to an infinite bus system. This setup is used to generate synthetic data in grid-following control mode, enabling the evaluation of these methods in effectively capturing system dynamics.

Original language	English (US)
Title of host publication	2026 IEEE Texas Power and Energy Conference, TPEC 2026
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331557201
DOIs	https://doi.org/10.1109/TPEC67884.2026.11513098
State	Published - 2026
Event	2026 IEEE Texas Power and Energy Conference, TPEC 2026 - College Station, United States Duration: Feb 9 2026 → Feb 10 2026

Publication series

Name	2026 IEEE Texas Power and Energy Conference, TPEC 2026

Conference

Conference	2026 IEEE Texas Power and Energy Conference, TPEC 2026
Country/Territory	United States
City	College Station
Period	2/9/26 → 2/10/26

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Control and Optimization
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Computer Networks and Communications

Keywords

Deep symbolic regression
SINDy
grid-connected converter
grid-following control
sparse identification of nonlinear dynamics
symbolic regression
system identification

Access to Document

10.1109/TPEC67884.2026.11513098

Cite this

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title = "Data-Driven Modeling of Grid-Following Control in Grid-Connected Converters",

abstract = "As power systems evolve with the integration of renewable energy sources and the implementation of smart grid technologies, there is an increasing need for flexible and scalable modeling approaches capable of accurately capturing the complex dynamics of modern grids. To meet this need, various methods, such as the sparse identification of nonlinear dynamics and deep symbolic regression, have been developed to identify dynamical systems directly from data. In this study, we examine the application of a converter-based resource as a replacement for a traditional generator within a lossless transmission line linked to an infinite bus system. This setup is used to generate synthetic data in grid-following control mode, enabling the evaluation of these methods in effectively capturing system dynamics.",

keywords = "Deep symbolic regression, SINDy, grid-connected converter, grid-following control, sparse identification of nonlinear dynamics, symbolic regression, system identification",

author = "Javadi, \{Amir Bahador\} and Philip Pong",

note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 2026 IEEE Texas Power and Energy Conference, TPEC 2026 ; Conference date: 09-02-2026 Through 10-02-2026",

year = "2026",

doi = "10.1109/TPEC67884.2026.11513098",

language = "English (US)",

series = "2026 IEEE Texas Power and Energy Conference, TPEC 2026",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2026 IEEE Texas Power and Energy Conference, TPEC 2026",

address = "United States",

}

Javadi, AB & Pong, P 2026, Data-Driven Modeling of Grid-Following Control in Grid-Connected Converters. in 2026 IEEE Texas Power and Energy Conference, TPEC 2026. 2026 IEEE Texas Power and Energy Conference, TPEC 2026, Institute of Electrical and Electronics Engineers Inc., 2026 IEEE Texas Power and Energy Conference, TPEC 2026, College Station, United States, 2/9/26. https://doi.org/10.1109/TPEC67884.2026.11513098

Data-Driven Modeling of Grid-Following Control in Grid-Connected Converters. / Javadi, Amir Bahador; Pong, Philip.
2026 IEEE Texas Power and Energy Conference, TPEC 2026. Institute of Electrical and Electronics Engineers Inc., 2026. (2026 IEEE Texas Power and Energy Conference, TPEC 2026).

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

TY - GEN

T1 - Data-Driven Modeling of Grid-Following Control in Grid-Connected Converters

AU - Javadi, Amir Bahador

AU - Pong, Philip

PY - 2026

Y1 - 2026

N2 - As power systems evolve with the integration of renewable energy sources and the implementation of smart grid technologies, there is an increasing need for flexible and scalable modeling approaches capable of accurately capturing the complex dynamics of modern grids. To meet this need, various methods, such as the sparse identification of nonlinear dynamics and deep symbolic regression, have been developed to identify dynamical systems directly from data. In this study, we examine the application of a converter-based resource as a replacement for a traditional generator within a lossless transmission line linked to an infinite bus system. This setup is used to generate synthetic data in grid-following control mode, enabling the evaluation of these methods in effectively capturing system dynamics.

AB - As power systems evolve with the integration of renewable energy sources and the implementation of smart grid technologies, there is an increasing need for flexible and scalable modeling approaches capable of accurately capturing the complex dynamics of modern grids. To meet this need, various methods, such as the sparse identification of nonlinear dynamics and deep symbolic regression, have been developed to identify dynamical systems directly from data. In this study, we examine the application of a converter-based resource as a replacement for a traditional generator within a lossless transmission line linked to an infinite bus system. This setup is used to generate synthetic data in grid-following control mode, enabling the evaluation of these methods in effectively capturing system dynamics.

KW - Deep symbolic regression

KW - SINDy

KW - grid-connected converter

KW - grid-following control

KW - sparse identification of nonlinear dynamics

KW - symbolic regression

KW - system identification

UR - https://www.scopus.com/pages/publications/105041435759

UR - https://www.scopus.com/pages/publications/105041435759#tab=citedBy

U2 - 10.1109/TPEC67884.2026.11513098

DO - 10.1109/TPEC67884.2026.11513098

M3 - Conference contribution

AN - SCOPUS:105041435759

T3 - 2026 IEEE Texas Power and Energy Conference, TPEC 2026

BT - 2026 IEEE Texas Power and Energy Conference, TPEC 2026

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2026 IEEE Texas Power and Energy Conference, TPEC 2026

Y2 - 9 February 2026 through 10 February 2026

ER -

Data-Driven Modeling of Grid-Following Control in Grid-Connected Converters

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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