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Updated: Jan 28, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
Published on: February 14, 2025
Intelligent RBF neural network-based control for dynamic stability and power control in renewable-integrated
Venkatesh Chiluka1, G G Raja Sekhar1, Ch Rami Reddy2,3
1Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India.
This study introduces a novel control strategy for hybrid renewable energy microgrids using a Radial Basis Function Neural Network (RBFNN) controller. The system enhances stability and control accuracy for efficient energy management.
Area of Science:
- Electrical Engineering
- Renewable Energy Systems
- Control Systems
Background:
- Microgrid energy management is complex due to differing AC/DC component operations, leading to frequency and voltage instability.
- Hybrid Renewable Energy Sources (HRES) integration presents unique control challenges in microgrids.
Purpose of the Study:
- To develop an advanced control and process strategy for microgrids incorporating HRES.
- To enhance real-time monitoring, optimization, and control of microgrid energy flow.
Main Methods:
- Utilizing a Radial Basis Function Neural Network (RBFNN) controller for overall system management.
- Employing a Z-source integrated coupled inductor boost (Z-SCIB) converter with Grey Lag Goose Optimization (GGO)-PI control for the Photovoltaic (PV) system.
- Implementing a Pulse Width Modulation (PWM) rectifier with PI control for the Doubly Fed Induction Generator (DFIG)-Wind Energy Conversion System (WECS).
- Integrating a bidirectional converter for battery storage system management on the DC link.
Main Results:
- Demonstrated significant improvements in microgrid system performance and stability.
- Achieved enhanced control accuracy under various operating conditions.
- Validated converter efficiency of [Formula: see text] through extensive MATLAB/Simulink simulations.
Conclusions:
- The proposed RBFNN-based control strategy effectively manages energy in HRES microgrids.
- The integrated system components and control methods ensure optimal power transfer and grid stability.
- The research provides a robust framework for advanced microgrid control and energy management.
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