MPPT Solar Using ANN, FL and P&O via Buck converter

Abstract

Environmental and economic concerns have led to the implementation of solar photo- voltaic energy system(PV system) to avoid and reduce the uses of fossil fuels. On the other hand, the negative thing is that the efficiency of solar panels deteriorates due to its association with weather conditions. Hence, MPPT technology must be used to ex- tract as much energy as possible to charge the batteries. The outputs obtained from a photovoltaic (PV) array cannot be connected directly to electronic devices. To regulate the output from the PV array, a DC-DC buck converter is provided to charge the battery from the solar array, and the converter plays an important role in tracking the maximum power point of the solar panel,after the completion of the simulation, a realistic prototype of the studied system was manufactured using Arduino Uno as a control unit. In this work, different algorithms based on classical and artificial AI methodologies are proposed to provide a comprehensive comparative analysis of the most commonly used MPPT AI algorithms, namely: Perturbation and Observation (P&O), Fuzzy Logic (FL), and Arti- ficial Neural Networks (ANN). The MATLAB/Simulink environment was used to realize the three algorithms, and Proteus and easyida for the manufacture and installation of the prototype.