arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2412.08065eess.SYcs.SY
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面向机器学习应用的、支持构网型逆变器的开源电力系统动态仿真器综述

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

AI总结:

针对构网型逆变器技术发展与电力系统机器学习应用需求,本文调研对比了多款支持GFM的开源电力系统动态仿真器,聚焦其在机器学习场景下的功能与性能,为相关研究与模型验证提供参考。

摘要:

构网型(GFM)逆变器技术的兴起,以及机器学习在电力系统中日益重要的作用,凸显了评估最新动态仿真器的必要性。开源仿真器在该领域具备独特优势:既免费又高度可定制,十分适用于科学研究以及对最新模型与方法的验证。本文对支持GFM的最新开源仿真器展开全面调研与对比,重点关注它们在机器学习应用中的功能与性能。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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