ArticleScientific reports2025
Intelligent power control using deep neural networks and regularized learning for shipboard microgrid.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Renewable generation units, along with storage systems, are being integrated into shipboard microgrids (SHMGs) for enhanced operational efficiency and cost-effective benefits. However, the direct current (DC) voltage profiles of SHMGs are affected by sudden changes in load, intermittent renewable inputs, and unmodeled dynamics, which necessitate advanced control strategies to address them. 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 across a wide range of operational situations. The proposed data-driven voltage regulator is developed in two stages: (i) an ultra-local model control (ULMC) is designed to stabilize the voltage outcomes of SHMG using the input/output (I/O) data. In this stage, a regularized actor-critic (RAC) using deep neural networks is adopted to adaptively adjust coefficients of ULMC, and (ii) in the next stage, the un-modeled phenomena and disturbances included in the marine power system are approximated by non-integer extended state observer (NIESO). In this approach, deep neural networks of RAC learning are trained in such a way that they obtain an optimal policy using a reward function that is defined based on the regulation requirements of SHMG. The proposed control framework provides this possibility for the system to have a robust estimation and compensation to tackle the largely unknown nonlinear disturbances and unmodeled dynamics. Hardware-in-the-loop (HiL) simulation using OPAL-RT has been employed as a powerful real-time testbed to evaluate the feasibility and applicability of the proposed voltage compensator under realistic operations of SHMG. HiL outcomes reveal that the proposed controller based on RAC achieves significant enhancement in performance indexes, with improvements of 44.08% over the fuzzy logic controller and 36.85% over the model predictive controller (MPC).
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