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Intelligent power control using deep neural networks and regularized learning for shipboard microgrid.

Wenhua Deng1, Kaixia Lu2, Xinxin Li3

  • 1Wuhan Railway Vocational College of Technology, Wuhan, 430205, Hubei, China.

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|October 29, 2025
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Summary

This study introduces an adaptive data-driven controller for shipboard microgrids (SHMGs) with hybrid energy storage units (HESUs). The novel controller enhances voltage stability and performance, outperforming existing methods in simulations.

Keywords:
Data-driven controlHybrid energy storage unitNon-integer extended state observer (NIESO)Regularized actor-critic (RAC)Shipboard microgrid

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Renewable Energy Integration

Background:

  • Shipboard microgrids (SHMGs) integrate renewable energy and storage for efficiency.
  • DC voltage stability in SHMGs is challenged by load variations, renewables, and unmodeled dynamics.
  • Advanced control strategies are crucial for robust voltage regulation in SHMGs.

Purpose of the Study:

  • To propose an adaptive data-driven controller for robust voltage regulation in SHMGs with hybrid energy storage units (HESUs).
  • To enhance the stability and performance of SHMGs under dynamic operational conditions.
  • To validate the controller's effectiveness using hardware-in-the-loop (HiL) simulations.

Main Methods:

  • Developed a two-stage data-driven voltage regulator.
  • Stage 1: Ultra-local model control (ULMC) stabilized by a regularized actor-critic (RAC) deep neural network.
  • Stage 2: Non-integer extended state observer (NIESO) approximated unmodeled dynamics and disturbances.

Main Results:

  • The proposed RAC-based controller demonstrated significant performance improvements.
  • Achieved 44.08% enhancement over fuzzy logic controllers and 36.85% over model predictive controllers (MPC).
  • Hardware-in-the-loop (HiL) simulations confirmed feasibility and applicability under realistic SHMG operations.

Conclusions:

  • The adaptive data-driven controller ensures robust voltage regulation in SHMGs.
  • The framework effectively estimates and compensates for unknown nonlinear disturbances and unmodeled dynamics.
  • The proposed controller offers a superior solution for SHMG voltage stability compared to traditional methods.