MATLAB script implementing a Neural Network MPPT for PV

Version 1.0.0 (1.89 KB) by PIRC
MATLAB script implementing a Neural Network MPPT for Solar PV systems trained using Particle Swarm Optimization (PSO).
Updated 11 Aug 2023

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The MATLAB code implements a technique to enhance the Maximum Power Point Tracking (MPPT) process in Solar Photovoltaic (PV) systems using a Neural Network. This neural network is trained using the Particle Swarm Optimization (PSO) algorithm, a nature-inspired optimization technique.
In simpler terms, the code creates a model that mimics the behavior of the solar panels and their power output. The neural network is designed to learn how the power output of the panels changes with varying conditions like sunlight intensity and temperature. The PSO algorithm fine-tunes the parameters of the neural network to optimize its accuracy in predicting the maximum power point where the panels generate the most energy.
By using this code, the efficiency of the MPPT process is improved. The neural network can adapt to changing environmental factors, leading to better energy capture from the solar panels. This can result in increased overall energy output from the PV system, contributing to more efficient and effective utilization of solar energy.
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Cite As

PIRC (2024). MATLAB script implementing a Neural Network MPPT for PV (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2023a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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