An Extended Generalized Average Modeling Framework for Power Converters

The generalized averaged modeling (GAM) technique is traditionally employed to capture the dynamic performance of power electronic converters. This article proposes an improved version of it, named the extended-GAM (EGAM) technique, which supports the multiplication of two double Fourier series (DFS) signals in the time domain. Multiplication of DFS signals in the time domain translates to the 2-D convolution of coefficients of the DFS terms of their equivalent discrete Fourier image representations. Thus, the proposed EGAM technique, capable of capturing many harmonics present in the output of a power converter, effectively captures the dynamic behavior of power converters excited by two distinct frequencies. The proposed technique is then converted into an algorithm suitable for numerical platforms, which typically use ordinary differential equation solvers. The proposed algorithm is validated based on the observations of the effects of harmonic truncation. The efficacy of the proposed technique is assessed through a case study, wherein a single-phase inverter employs LC filters on both the dc link and the ac side. Finally, it is shown that the results obtained with the proposed method show an excellent congruence between simulation and hardware experimental models. Additionally, the proposed algorithm is packaged into a MATLAB toolbox and shared for future implementations.