Extraction of Dynamic Low-Order Nonlinear Inductor Models Based on Steady State Solutions

Three methods for extracting low-order dynamic models of nonlinear magnetic devices from a high-order physics-based (finite element) model are presented and compared. The proposed nonlinear model order reduction (NMOR) methods are based on the piecewise linear approach with the Krylov subspace projection using various training processes, various projection processes, and various weighting processes. These NMOR methods enable physics-based models to be implemented without heuristic assumptions or excessive computation. They are demonstrated with an example inductor model and verified experimentally. Simulation results show that the original, nonlinear high-order system is well represented by a piecewise set of connected low-order, linear systems. Synthesis of these methods can be fully automated so the end user can rapidly model new devices without repeating sophisticated mathematics. This work provides a foundation for modeling more complicated magnetic devices for power electronics, such as multi-phase coupled inductors, actuators, and rotary machines