Overview
AI-based adaptive RBF neural network MPPT for a PV-wind-battery-supercapacitor hybrid DC microgrid in MATLAB/Simulink. This article explains the simulation direction, expected waveform evidence and research relevance for engineering students, project clients and research scholars.
Simulation Objective
The main objective is to develop and validate a MATLAB/Simulink workflow that demonstrates the control strategy, subsystem behaviour and output response for the selected engineering model.
Methodology
Adaptive RBF neural-network learning is used to track maximum power under renewable variation while coordinating hybrid storage and DC-bus regulation.
Expected Outputs
- PV power, wind power, DC-link voltage, battery SOC, supercapacitor current, MPPT tracking response and hybrid microgrid power balance.
- Scope-based waveform comparison and steady-state/transient response checks.
- Result interpretation suitable for report, thesis or project explanation.
Applications
renewable microgrid research, hybrid energy storage control, smart-grid studies, PhD optimization work and OEM renewable-power validation.
Project Page
View the complete project page with video output and contact CTA: AI-Based Adaptive RBF Neural Network MPPT for PV–Wind–Battery–Supercapacitor Hybrid DC Microgrid MATLAB Simulink.
Send your project title, software version, required graphs and deadline.
Contact MATLAB Projects Code →