Artificial Intelligence-Based Power Management System for a DC Micro-Grid

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 10

Year of Publication : 2025

Authors : G. Prakash, R. Dharmaprakash, M.S. Sujatha, Shafreen Parvez S, Jaswanth Kumar Reddy

10.14445/23488379/IJEEE-V12I10P109

How to Cite?

G. Prakash, R. Dharmaprakash, M.S. Sujatha, Shafreen Parvez S, Jaswanth Kumar Reddy, "Artificial Intelligence-Based Power Management System for a DC Micro-Grid," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 10, pp. 110-122, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I10P109

Abstract:

An advanced approach to a power management system for DC microgrids with a combined solar PV and wind energy system and battery storage is presented here. It began by implementing controllers, including fuzzy logic and FO-PID-based control for renewable management with double FO-PID controls for battery operations. Power-output stabilization was obtained effectively by the fuzzy-FO-PID system, but equipment stability suffered from rapid power fluctuations. The controller system installed in source-side converters seeks to extract maximum power output from wind turbines and PV arrays, improving microgrid stability and reliability. A FO-PID controller is an additional control mechanism that specifically operates on the battery storage system to monitor its charging and discharging activities for enhanced battery operation and balanced energy distribution. The research focuses on enhancing the operational connection between the costs of renewable energy systems throughout the system. An ANN-based Radial Basis Neural Network (RBN) controller was designed to regulate solar and wind power outputs, but the battery management system kept its double FO-PID controller. The ANN-RBN controller formed an advanced control system because its adaptive learning capabilities enhanced tracking of dynamic events, along with minimizing voltage swings to enhance power quality. MATLAB/Simulink simulations will be performed to evaluate the ANN-RBN controller’s effectiveness in stabilizing DC microgrid performance, with comparisons made against the conventional fuzzy-FO-PID controller in terms of voltage stability and disturbance response under varying operating conditions. The novelty of the work lies in integrating a hybrid control scheme that combines an ANN-RBN controller for adaptive renewable power regulation with a double FO-PID strategy for battery management, enabling stable energy distribution alongside improved power quality.

Keywords:

DC-Microgrid, Fractional Order Proportional Integral Derivative Control, Fuzzy Logic Control, Radial Basis Neural Network, Renewable Energy Sources, Storage Battery.

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