Estimating Transient Stability Regions of Large-Scale Power Systems Part Ⅰ:Koopman Operator and Reduced-Order Model  

作　　者：Yuqing Lin Tianhao Wen Lei Chen Q.H.Wu Yang Liu 

机构地区：[1]School of Electric Power Engineering,South China University of Technology,Guangzhou 5106440,China

出　　处：《CSEE Journal of Power and Energy Systems》2025年第1期24-37,共14页中国电机工程学会电力与能源系统学报(英文)

摘　　要：This paper presents an estimation of transient stability regions for large-scale power systems.In Part I,a Koopman operator based model reduction(KOMR)method is proposed to derive a low-order dynamical model with reasonable accuracy for transient stability analysis of large-scale power systems.Unlike traditional reduction methods based on linearized models,the proposed method does not require linearization,but captures dominant modes of the original nonlinear dynamics by employing a Koopman operator defined in an infinite-dimensional observable space.Combined with the Galerkin projection,the obtained dominant Koopman eigenvalues and modes produce a reduced-order nonlinear model.To approximate the Koopman operator with sufficient accuracy,we introduce a Polynomial-based Multi-trajectory Kernel Dynamic Mode Decomposition(PMK-DMD)algorithm,which outperforms traditional DMD in various scenarios.In the end,the proposed method is applied to the IEEE 10-machine-39-bus power system and IEEE 16-machine-68-bus power system,which demonstrates that our method is significantly superior to the modal analysis method in both qualitative and quantitative aspects.

关 键 词：Data driven method dynamic mode decomposition Koopman operator model reduction power systems 

分 类 号：TM614[电气工程—电力系统及自动化] TM732