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

TL;DR

This paper surveys open-source power system dynamic simulators supporting grid-forming inverters, emphasizing their suitability for machine learning research and highlighting their capabilities and performance.

Contribution

It provides the first comprehensive comparison of open-source GFM-compatible simulators tailored for machine learning applications in power systems.

Findings

Open-source simulators vary in capabilities and performance.

Most simulators support key features needed for GFM and machine learning.

The survey identifies gaps and future directions for simulator development.

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.