Dynamic Model Identification via Hankel Matrix Fitting: Synchronous Generators and IBRs
Dynamic Model Identification via Hankel Matrix Fitting: Synchronous Generators and IBRs
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DOI：
10.1109/naps56150.2022.10012225
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Abdullah Alassaf;Lingling Fan
中科院分区：
文献类型：
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作者：
Abdullah Alassaf;Lingling Fan
本文提出了基于时域测量数据的同步发电机和逆变器为基础的资源（IBR）的动态模型参数估计。虽然预测误差法（PEM）是一种众所周知且流行的方法，但它需要对应该处于收敛域中的参数进行良好的初始猜测。最近，系统辨识界在考虑低秩数据汉克尔矩阵的特性来改进PEM方法方面取得了重大进展。进而，估计问题可以被公式化为秩约束优化问题，并且进一步为凸规划差分（DCP）问题。本文采用数据汉克尔矩阵拟合策略，建立了同步发电机和IBR参数估计问题的数学模型。给出了这两个算例，结果令人满意。
This paper presents time-domain measurement data-based dynamic model parameter estimation for synchronous generators and inverter-based resources (IBRs). While prediction error method (PEM) is a well-known and popular method, it requires a good initial guess of parameters which should be in the domain of convergence. Recently, the system identification community has made significant progress in improving the PEM method by taking into consideration of the characteristics of the low-rank data Hankel matrix. In turn, an estimation problem can be formulated as a rank-constraint optimization problem, and further a difference of convex programming (DCP) problem. This paper adopted the data Hankel matrix fitting strategy and developed the problem formulation for the parameter estimation problems for synchronous generators and IBRs. These two examples are presented and the results are satisfying.