新型电力系统的高比例电力电子化特征使得系统建模分析出现“维数灾”难题。现有的模型降阶方法中，理论分析法虽然针对性强，但存在通用性不足的问题；奇异摄动法的状态量选取原则同线性化模型相关，无法准确把握完整动态特性。针对状态量选取原则进行改进，利用数据驱动获取完整动态特性，通过最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）进行状态量选取，并根据物理信息补充重要状态量，应用摄动法完成系统降维。最后，在典型变流器并网系统进行应用，降维系统能够反映原系统小扰动稳定特性，证明了该方法的有效性。

The high proportion of power electronics in new power systems introduces the "dimensionality disaster" challenge in system modeling and analysis. Among existing model order reduction methods, theoretical analysis approaches are highly targeted but suffer from insufficient universality. The state variable selection principle of the singular perturbation method is tied to linearized models, making it difficult to accurately capture comprehensive dynamic characteristics. To address this, the state variable selection principle is improved by leveraging data-driven techniques to obtain full dynamic characteristics. The least absolute shrinkage and selection operator (LASSO) is employed for state variable selection, supplemented with critical state variables based on physical information. Perturbation methods are then applied to achieve system dimensionality reduction. Finally, the approach is tested in a typical grid-connected converter system, demonstrating that the reduced-order system can reflect the small-disturbance stability characteristics of the original system, validating the method′s effectiveness.