The oscillatory stability analysis of multi-converter-fed systems(MCFSs)with modest computational resources needs a precise parametric reduced-order impedance model(PROIM).However,the traditional Krylov subspace based...The oscillatory stability analysis of multi-converter-fed systems(MCFSs)with modest computational resources needs a precise parametric reduced-order impedance model(PROIM).However,the traditional Krylov subspace based parametric model order reduction(KS-PMOR)method has difficulty in building precise PROIM for MCFSs.This is because the factors related to the errors of PROIM are complicated and coupled.To fill this gap,the factors associated with the accuracy of the KS-PMOR method are estimated by defining three indicators:the convergence error,cumulative error,and rank of projection matrix.Using the three indicators,a frequency-domain adaptive parametric model order reduction(FDA-PMOR)method is developed to form the precise PROIM of MCFSs for the accurate and fast oscillatory stability analysis.The accuracy of the obtained PROIM using the proposed FDA-PMOR method and its efficiency in actual oscillatory stability analysis are validated by three MCFSs with different scales,i.e.,a small-scale MCFS with four paralleled converter-based renewable energy generators(CREGs),a real-time simulation-based MCFS with eighteen paralleled CREGs,and a larger MCFS with ninety paralleled CREGs.展开更多
基金supported by Open Fund of State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems(China Electric Power Research Institute)(No.NYB51202201695)National Natural Science Foundation of China(No.51677050)111 Project(No.BP0719039).
文摘The oscillatory stability analysis of multi-converter-fed systems(MCFSs)with modest computational resources needs a precise parametric reduced-order impedance model(PROIM).However,the traditional Krylov subspace based parametric model order reduction(KS-PMOR)method has difficulty in building precise PROIM for MCFSs.This is because the factors related to the errors of PROIM are complicated and coupled.To fill this gap,the factors associated with the accuracy of the KS-PMOR method are estimated by defining three indicators:the convergence error,cumulative error,and rank of projection matrix.Using the three indicators,a frequency-domain adaptive parametric model order reduction(FDA-PMOR)method is developed to form the precise PROIM of MCFSs for the accurate and fast oscillatory stability analysis.The accuracy of the obtained PROIM using the proposed FDA-PMOR method and its efficiency in actual oscillatory stability analysis are validated by three MCFSs with different scales,i.e.,a small-scale MCFS with four paralleled converter-based renewable energy generators(CREGs),a real-time simulation-based MCFS with eighteen paralleled CREGs,and a larger MCFS with ninety paralleled CREGs.
