A System Identification-based Modeling Framework of Bidirectional DC-DC Converters for Power Grids

This paper proposes a system identification framework based on eigensystem realization to accurately model power electronic converters. The proposed framework affords an energy-based optimal reduction method to precisely identify the dynamics of power electronic converters from simulated or actual raw data measured at the converter's ports. This method does not require any prior knowledge of the topology or internal parameters of the converter to derive the system modal information. The accuracy and feasibility of the proposed method are exhaustively evaluated via simulations and practical tests on a software-simulated and hardware-implemented dual active bridge (DAB) converter under steady-state and transient conditions. After various comparisons with the Fourier series-based generalized average model, switching model, and experimental measurements, the proposed method attains a root mean square error (RMSE) of less than 1% with respect to the actual raw data. Moreover, the computational effort is reduced to 1/8.6 of the Fourier series-based model.