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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.

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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.