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In order to break limited step-down ratio range and poor regulation capacity in the conventional resonant switched-capacitor converters for rectifier application, a single-stage ac/dc rectifier based on the dual-resonance switched-capacitor converter is proposed in this article. Through utilizing a dual-resonant structure and nonoverlapping strategy to realize wide continuous voltage gain of the proposed converter, high power factor (PF) and low total harmonic distortion (THD) based on the narrow dead zone are guaranteed by operating at high step-down conversion ratio in this converter topology. Moreover, the voltage gain is concentrated between resonant frequency and double resonant frequency to achieve a narrow switching frequency range for rectifier application. Furthermore, operating at flat voltage stress curves to obtain low voltage stress character. Comprehensive mode analysis, input PF and THD, and stress analysis are given, and analysis results indicate that the proposed power factor correction converter can work with soft turn on and zero-current turn off; high efficiency is realized. Finally, an experimental prototype with 85–130 Vac input and 47 Vdc/44 W output is built and tested for verifying the operation modes and PFC performance, and the comparison between different rectifiers and the proposed one is provided. [ABSTRACT FROM AUTHOR]

Aim to improve the power efficiency of the dual-active-bridge (DAB) dc–dc converter, an efficiency optimization scheme with triple-phase-shift (TPS) modulation using reinforcement learning (RL) is proposed in this article. More specifically, the Q-learning algorithm, as a typical algorithm of the RL, is applied to train an agent offline to obtain an optimized modulation strategy, and then the trained agent provides control decisions online in a real-time manner for the DAB dc–dc converter according to the current operating environment. The main objective is to obtain the optimal phase-shift angles for the DAB dc–dc converter, which can achieve the maximum power efficiency by reducing the power losses. Moreover, all possible operation modes of the TPS modulation are considered during the offline training process of the Q-learning algorithm. Thus, the cumbersome process for selecting the optimal operation mode in the conventional schemes can be circumvented successfully. Based on these merits, the proposed efficiency optimization scheme using the RL can realize the excellent performances for the whole load conditions and voltage conversion ratios. Finally, a 1.2-KW prototyped is built, and the simulation and the experimental results demonstrate that the power efficiency can be improved by using the optimization scheme based on the RL. [ABSTRACT FROM AUTHOR]