Şebeke bağlı PV sistemlerde maksimum güç noktası takibi ve aktif/reaktif güç kontrolü için sinir ağlarının tasarlanması ve simülasyonu

This thesis explores the application of Neural Network (NN) in the management of gridconnected Photo Voltaic (PV) system. This study aims to improve the performance and reliability of PV systems using NN capabilities. The controller modelling using MATLAB/Simulink, the first section of the thesis designs and implements a Maximum Power Point Tracker (MPPT) control system based on NN, designed to dynamically adapt to variable solar radiation intensity due to weather conditions. it also raises the voltage from 260 V to 350 V despite fluctuations in radiation intensity. The result obtained indicates that the boost converter effectively raised the PV voltage and maintain it at a certain level while working under the guidance of the NN. The final section discusses active and reactive power control. The algorithm provides local reactive power compensation making it economically viable. We use five different performance scenarios of the proposed control method are tried, and the NN controller shows remarkable flexibility and quickly adapting to fluctuations in load and radiation. The inverter voltage of 230V was equivalent to the mains voltage due to their parallel connection. The Total Harmonic Distortion (THD) of the grid current under all operating conditions was measured at less than 1.86%. In addition, it has been observed that the success rate of NN is more than 99%. This thesis provides insight into the potential of NN in renewable energy applications by using NN, contributing to the development of a more sustainable and stable energy grid, and obtaining an integrated system that can be integrated with the grid.