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 …

A DC microgrid's tightly regulated DC/DC converter encounters significant challenges in
voltage stability, primarily due to the negative incremental resistance of constant power …

Data-driven reinforcement-learning-based controller schemes have much potential to aid
the design of model-free control algorithms that can be trained without the necessity of plant …

Wireless charging technology for electric vehicles (EVs) is gaining popularity as a result of
advancements in battery technology and government incentives. It offers convenience …

This article proposes a distributed uniform control approach for a dc solid state transformer
(DCSST) that feeds constant power loads. The proposed approach utilizes a multiagent …

To accommodate constant power loads (CPLs) with varying degrees of disturbances levels
in dc microgrid systems, the adaptability of existing robust control strategies should be …

Recent application studies of deep reinforcement learning (DRL) in power electronic
systems have successfully demonstrated its superiority over conventional model-based …

This study is concerned with an event-driven brain emotional online learning approach for
finite control-set model predictive control (FCS-MPC) framework subject to system …

Over the past few years, the use of DC-DC buck-boost converters for Photovoltaic (PV) in
renewable energy applications has increased for better results. One of the main issues with …

The dc solid-state transformer (dcSST) plays a vital role in interconnecting diverse dc
sources and loads in dc microgrids. However, output voltage regulation and submodule …