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arXiv 2511.03494eess.SYcs.SY
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并网变换器中跟网控制的数据驱动建模

Data-driven Modeling of Grid-following Control in Grid-connected Converters

Amir Bahador Javadi, Philip Pong

AI总结:

本研究针对现代电网复杂动态建模需求,评估非线性动力学稀疏识别与深度符号回归等数据驱动方法在跟网控制模式下捕捉系统动态的有效性。

摘要:

随着电力系统因可再生能源的集成和智能电网技术的实施而演进,对能够准确捕捉现代电网复杂动态的灵活且可扩展的建模方法的需求日益增加。为满足此需求,已开发出多种方法,如非线性动力学的稀疏识别和深度符号回归,以直接从数据中识别动力系统。在本研究中,我们考察了将基于变换器的资源作为替代传统发电机的方法,该资源置于连接至无穷大母线系统的无损传输线中。此设置用于在跟网控制模式下生成合成数据,从而评估这些方法在有效捕捉系统动态方面的能力。

英文摘要:

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.

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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