A Generalized State Space Average Model for Parallel DC-to-DC Converters

The high potentiality of integrating renewable energies,such as photovoltaic,into a modern electrical microgrid system,using DC-to-DC converters,raises some issues associated with controller loop design and system stability.The generalized state space average model(GSSAM)concept was consequently introduced to design a DC-to-DC converter controller in order to evaluate DC-to-DC converter performance and to conduct stability studies.This paper presents a GSSAM for parallel DC-to-DC converters,namely:buck,boost,and buck-boost converters.The rationale of this study is that modern electrical systems,such as DC networks,hybrid microgrids,and electric ships,are formed by parallel DC-to-DC converters with separate DC input sources.Therefore,this paper proposes a GSSAM for any number of parallel DC-to-DC converters.The proposed GSSAM is validated and investigated in a time-domain simulation environment,namely a MATLAB/SIMULINK.The study compares the steady-state,transient,and oscillatory performance of the state-space average model with a fully detailed switching model.

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.

Review of General Modeling Approaches of Power Converters

The modeling approaches of power converters occupy an important position in power electronic systems and have made considerable progress over the past years.Continuous modeling approaches and linearization techniques are reviewed,including the state-space average model,generalized average model,averaged small-signal model,and describing function method.A Buck converter with PWM modulation and voltage-mode control is taken as an example to compare the advantages and disadvantages of different methods through simulation analysis.Moreover,the corresponding equivalent circuit with an intuitive physical meaning of state-space average model,generalized average model,and averaged small-signal model is given.The results point out that the generalized average model can improve the modeling accuracy based on the state-space average model.In the linearization techniques,the averaged small-signal model reflects accuracy at low frequencies,but introduces phase lag in the high-frequency region.The describing function method is derived from harmonic linearization,which takes into account the sideband effect and improves the modeling accuracy at high frequencies.