A review on the applications of reinforcement learning control for power electronic converters
P Chen, J Zhao, K Liu, J Zhou, K Dong… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
In modern micro-grid systems, the control of power electronic converters faces numerous
challenges, including the uncertainty of parameters of the controlled objects, variations in …
challenges, including the uncertainty of parameters of the controlled objects, variations in …
A survey on multi-active bridge DC-DC converters: Power flow decoupling techniques, applications, and challenges
Multi-port DC-DC converters are a promising solution for a wide range of applications
involving multiple DC sources, storage elements, and loads. Multi-active bridge (MAB) …
involving multiple DC sources, storage elements, and loads. Multi-active bridge (MAB) …
Multiple Active Bridge Converter Controlled by a Feed-Forward Compensator
Multiple active bridge (MAB) converters have gained relevance in applications such as
photovoltaic systems, microgrids, aircraft, solid-state transformers, and electric vehicles. One …
photovoltaic systems, microgrids, aircraft, solid-state transformers, and electric vehicles. One …
Deep reinforcement learning-based power flow control for triple active bridge converter
This paper presents a power flow control strategy based on deep reinforcement learning
(DRL) for triple active bridge (TAB) converter. The DRL-based agent is designed to control …
(DRL) for triple active bridge (TAB) converter. The DRL-based agent is designed to control …