Structure Preserving Nonlinear Reduced Order Modeling Technique for Power Systems

Abstract

This manuscript presents a reduced-order modeling framework that preserves the structure of nonlinear power system models. The offline reduced manifold is formed using the second-order nonlinear moment-matching (SO-NLMM) technique. A hyper-reduction of the nonlinear inner-products is then performed utilizing the discrete empirical interpolation method (DEIM). The overall scheme is used to obtain nonlinear reduced models for large-scale power system models. The results present a significant saving in the CPU times while preserving the second-order structure of the original model.