ANN-Based Adaptive Power Management Strategy for Hybrid PV-Wind LVDC Microgrids

Paper ID: EIJTEM_2025_12_4_134-145

Author's Name: Medi Vamshi, C Radhacharan

Volume: 12

Issue: 4

Year: 2025

Page No: 134-145

Abstract:

In this study, we provide a unified and adaptive power management strategy for a microgrid that uses wind and PV together to generate low-voltage direct current (LVDC). By combining high-energy batteries with high-power supercapacitors, a hybrid energy storage system (HESS) is implemented to efficiently handle the variations in renewable production and dynamic load needs. To ensure effective operation under fluctuating generation and demand situations, the proposed Supervisory Power Management Scheme (SPMS) employs an ANN-based controller to intelligently and dynamically coordinate power flow inside the microgrid. The system is able to choose the right control actions for energy distribution and storage usage on its own since the ANN controller is taught to identify and react to diverse operating conditions. By efficiently distributing transient loads throughout the HESS, this allows for rapid DC bus voltage stabilization, guarantees power balance, and decreases battery strain. The suggested Supervisory Power Management System (SPMS) is validated by simulation results under several operating circumstances, proving its performance. The autonomous LVDC microgrid has better voltage stability, more effective mode transition, and better overall performance, according to the comparison findings.

Keywords: Hybrid energy storage system (HESS), supervisory power management scheme (SPMS), artificial neural network (ANN), Low-voltage direct current (LVDC) microgrid, photovoltaic (PV), wind energy, DC bus voltage regulation, intelligent control, renewable energy

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