基于奇异摄动理论的双馈风机并网系统降维建模方法
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中国石油大学华东新能源学院

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基金项目:

国家自然科学基金(52077222);山东省自然科学基金(ZR2020ME202)。


Reduced Order Modeling Method of Weak AC Grid-Connected DFIG Wind Turbines Based on Singlar Perturbation Theory
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College of New Energy,China University of PetroleumEast China

Fund Project:

National Natural Science Foundation of China(52077222), National Natural Science Foundation of Shandong Province(ZR2020ME202)

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    摘要:

    双馈风机(DFIG)并网系统详细模型具有维数高、非线性等特点,给系统稳定性分析、故障穿越控制等带来很大困难。尽管目前已有不少关于DFIG并网系统模型降维方法的研究,但依然存在模型维数相对较高的问题。对此,本文提出了一种基于奇异摄动理论的降维建模方法。首先,建立了弱电网下DFIG并网系统的12维详细模型,通过线性化得到其状态空间模型;之后,基于奇异摄动降阶理论结合参与因子分析将各状态变量划分到多个时间尺度下,分离快慢变量,推演获得系统的4维模型。降维前后的特征值对比表明,4维模型可以很好地反映全阶模型的主导动态特性,同时能准确反映弱电网下的风机并网稳定性。仿真和实验结果验证了降维方法的正确性及降维模型在稳定性分析上的有效性。

    Abstract:

    The mathematical model of wake AC grid-connected DFIG -based wind turbines has the characteristics of high order, nonlinearity, which brings great difficulties to system stability analysis and fault ride-through control. Although there have been many researches on the di-mensionality reduction method of DFIG model, the model dimension is still high. In this paper, a dimension reduction modeling method based on singular perturbation theory is proposed. Firstly, the 12th-order detailed model of DFIG is established, and its state space model is obtained by linearization. Then, based on the singular perturbation reduction theory and participation factor analysis, the state variables of the state space model are divided into multiple time scales, thereby the speed variables are separated, and the 4th-order model of the system is deduced. The comparison of eigenvalues before and after dimension reduction shows that the 4th-order model can well reflect the dominant dynamic characteristics of the full-order model, and can accurately reflect the stability of DFIG-based wind turbines in weak AC grid. Simulation and experimental results verify the accuracy of the dimension reduction method and the effectiveness of the reduced order model in stability analysis.

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  • 收稿日期:2022-06-15
  • 最后修改日期:2022-08-02
  • 录用日期:2022-08-08
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