Aiming at the challenges in existing data-driven methods, this paper presents NestT, a nested AI framework for the steady-state modeling of power converters in the time domain. NestT utilizes an extreme gradient boosting for initial status estimation and a gated-recurrent-unit net with layer normalization for temporal modeling. Synchronization and reset processes are included to improve training and enable cyclic modeling. The proposed NestT framework is validated through 1-kW hardware experiments. NestT strives to pave the way for increasing the penetration of AI in the modeling of power converters.
