AI-Based RBF MPPT for PV–Wind–Battery–Supercapacitor DC Microgrid | MATLAB Simulink
https://www.youtube.com/watch?v=c3l6pa_bzRs
This video presents an Online Adaptive Radial Basis Function Neural Network MPPT controller for a PV–Wind–Battery–Supercapacitor hybrid 48 V DC microgrid developed in MATLAB/Simulink.
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The proposed RBF neural network continuously learns the nonlinear operating characteristics of the photovoltaic system and adjusts the converter duty cycle to extract maximum available solar power under changing irradiation, temperature and load conditions.
The hybrid DC microgrid integrates:
✅ Photovoltaic energy system
✅ Wind energy conversion system
✅ 48 V DC bus
✅ Battery energy storage system
✅ Supercapacitor energy storage
✅ Bidirectional DC–DC converters
✅ Online adaptive RBF neural network MPPT
✅ DC-bus voltage regulation
✅ Hybrid energy-management control
✅ Variable renewable generation and load conditions
Unlike conventional Perturb and Observe MPPT, the adaptive RBF neural network can provide faster tracking, reduced steady-state oscillations and improved response under rapidly changing environmental conditions.
The battery supports long-term energy balancing, while the supercapacitor supplies or absorbs short-duration transient power. This coordinated energy-storage structure improves DC-bus stability, renewable-energy utilisation and overall microgrid reliability.
Main objectives
• Develop an online adaptive RBF neural network MPPT controller
• Maximise photovoltaic power under changing conditions
• Maintain the DC bus close to the 48 V reference
• Coordinate battery and supercapacitor power sharing
• Reduce voltage fluctuations during load and generation changes
• Improve MPPT speed and steady-state performance
• Demonstrate AI-based control for an off-grid hybrid DC microgrid
Applications
• Standalone renewable-energy systems
• Rural and remote-area electrification
• Telecom DC power systems
• Electric-vehicle charging infrastructure
• Smart buildings and residential microgrids
• Research on intelligent MPPT and energy management
• PhD, master’s and final-year engineering projects
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The simulation and results shown are intended for educational, demonstration and research-support purposes. Final performance may vary according to model parameters, operating conditions and controller tuning.
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