A Survey of Open-Source Power System Dynamic Simulators with Grid-Forming Inverter for Machine Learning Applications

Tong Su; Jiangkai Peng; Alaa Selim; Junbo Zhao; Jin Tan

doi:10.1109/PESGM52009.2025.11225483

A Survey of Open-Source Power System Dynamic Simulators with Grid-Forming Inverter for Machine Learning Applications

Tong Su
, Jiangkai Peng
, Alaa Selim
, Junbo Zhao
, Jin Tan

Power Systems Engineering

Research output: Contribution to conference › Paper

Abstract

The emergence of grid-forming (GFM) inverter technology and the increasing role of machine learning in power systems highlight the need for evaluating the latest dynamic simulators. Open-source simulators offer distinct advantages in this field, being both free and highly customizable, which makes them well-suited for scientific research and validation of the latest models and methods. This paper provides a comprehensive survey and comparison of the latest open-source simulators that support GFM, with a focus on their capabilities and performance in machine-learning applications.

Original language	American English
Number of pages	5
DOIs	https://doi.org/10.1109/PESGM52009.2025.11225483
State	Published - 2025
Event	2025 IEEE PES General Meeting - Austin, Texas Duration: 27 Jul 2025 → 31 Jul 2025

Conference

Conference	2025 IEEE PES General Meeting
City	Austin, Texas
Period	27/07/25 → 31/07/25

NLR Publication Number

NLR/CP-5D00-92206

Keywords

grid-forming inverter
machine learning application
open-source power system dynamic simulator

Access to Document

10.1109/PESGM52009.2025.11225483

Cite this

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title = "A Survey of Open-Source Power System Dynamic Simulators with Grid-Forming Inverter for Machine Learning Applications",

abstract = "The emergence of grid-forming (GFM) inverter technology and the increasing role of machine learning in power systems highlight the need for evaluating the latest dynamic simulators. Open-source simulators offer distinct advantages in this field, being both free and highly customizable, which makes them well-suited for scientific research and validation of the latest models and methods. This paper provides a comprehensive survey and comparison of the latest open-source simulators that support GFM, with a focus on their capabilities and performance in machine-learning applications.",

keywords = "grid-forming inverter, machine learning application, open-source power system dynamic simulator",

author = "Tong Su and Jiangkai Peng and Alaa Selim and Junbo Zhao and Jin Tan",

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language = "American English",

note = "2025 IEEE PES General Meeting ; Conference date: 27-07-2025 Through 31-07-2025",

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AU - Su, Tong

AU - Peng, Jiangkai

AU - Selim, Alaa

AU - Zhao, Junbo

AU - Tan, Jin

PY - 2025

Y1 - 2025

N2 - The emergence of grid-forming (GFM) inverter technology and the increasing role of machine learning in power systems highlight the need for evaluating the latest dynamic simulators. Open-source simulators offer distinct advantages in this field, being both free and highly customizable, which makes them well-suited for scientific research and validation of the latest models and methods. This paper provides a comprehensive survey and comparison of the latest open-source simulators that support GFM, with a focus on their capabilities and performance in machine-learning applications.

AB - The emergence of grid-forming (GFM) inverter technology and the increasing role of machine learning in power systems highlight the need for evaluating the latest dynamic simulators. Open-source simulators offer distinct advantages in this field, being both free and highly customizable, which makes them well-suited for scientific research and validation of the latest models and methods. This paper provides a comprehensive survey and comparison of the latest open-source simulators that support GFM, with a focus on their capabilities and performance in machine-learning applications.

KW - grid-forming inverter

KW - machine learning application

KW - open-source power system dynamic simulator

U2 - 10.1109/PESGM52009.2025.11225483

DO - 10.1109/PESGM52009.2025.11225483

M3 - Paper

T2 - 2025 IEEE PES General Meeting

Y2 - 27 July 2025 through 31 July 2025

ER -

A Survey of Open-Source Power System Dynamic Simulators with Grid-Forming Inverter for Machine Learning Applications

Abstract

Conference

NLR Publication Number

Keywords

Access to Document

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Cite this