With the development of artificial intelligence, researchers have begun to apply reinforcement learning to control reactive power distribution of DGs in ship AC microgrids.
To address the above problem, an adaptive data-driven controller has been proposed for voltage regulation of SHMGs with hybrid energy storage units (HESUs) to preserve robust stability
The study uses a cruise ship sailing in the Baltic Sea as a case to verify the potential of the proposed shipboard DC microgrid system and hierarchical EMS for ensuring stable ship operation,
New design and optimization methodologies, based on new dynamic system model, are presented and discussed. Power system performance has been assessed and validated through real-time hardware
We provided the analytical solution for implementing proposed optimal design of hierarchical control for a multi-DG and renewable energy resources (RESs) integration-based
Abstract owards more electric ships with sustainable designs can help in reducing the carbon footprint of the global transportation systems. Ship networks can be based on AC/DC microgrids with resilient
This study presented a robust hierarchical control architecture for hybrid AC/DC shipboard microgrids, integrating a conventional droop-based PI controller at the primary level with an SMC
The system combines ultra-local model control with regularized actor-critic learning using deep neural networks, along with a non-integer extended state observer to handle unmodeled
Smart Microgrid - Combines distributed energy technologies with intelligent software to intelligently monitor, proactively predict, actively manage and optimize energy supply & demand for a small-scale
A microgrid is a group of interconnected loads and distributed energy resources that acts as a single controllable entity with respect to the grid. It can connect and disconnect from the grid to
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