Data-Driven Sparse Model Identification of Inverter-Based Resources for Control in Smart Grids
Data-Driven Sparse Model Identification of Inverter-Based Resources for Control in Smart Grids
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DOI：
10.1109/icsmartgrid58556.2023.10170821
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
J. Khazaei;Wenxin Liu;F. Moazeni
中科院分区：
文献类型：
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作者：
J. Khazaei;Wenxin Liu;F. Moazeni
随着逆变器为主的电网的日益复杂，有一个新兴的需要，开发有效的建模工具，以确定动态的逆变器为基础的资源（IBR）互连到电网。本文利用数据驱动的方法来识别在现代电力系统中的并网IBR的动态。通过利用网格连接IBR的可用测量和状态的导数的估计，利用非线性动态的稀疏识别（SINDy）通过选择候选函数库来获得IBR动态。然后利用所获得的数据驱动模型设计控制器来调节并网IBR的有功功率和无功功率。时域仿真验证了所提出的数据驱动模型辨识方法在智能电网控制中的有效性。
With the growing complexity of inverter-dominated grids, there is an emerging need for developing effective modeling tools to identify the dynamics of inverter-based resources (IBRs) interconnected to the grid. This paper utilizes a data-driven approach for identifying the dynamics of grid-tie IBRs in modern power systems. By leveraging the available measurements of the grid-tie IBR and estimation of derivatives of the states, sparse identification of nonlinear dynamics (SINDy) is utilized to obtain the IBR dynamics by selecting a library of candidate functions. The obtained data-driven model is then utilized for designing controllers to regulate the active and reactive powers of the grid-tie IBR. Time-domain simulations validate the effectiveness of the proposed data-driven model identification approach for control purposes in smart grids.