Intelligent maximum power tracking and inverter hysteresis current control of grid-connected PV systems

Abstract

This paper proposes a maximum power point tracking scheme using neural networks for a grid connected photovoltaic system. The system is composed of a photovoltaic array, a boost converter, a three phase inverter and grid. The neural network proposed can predict the required terminal voltage of the array in order to obtain maximum power. The duty cycle is calculated and the boost converter switches are controlled. Hysteresis current technique is applied on the three phase inverter so that the output voltage of the converter remains constant at any required set point. The complete system is simulated using MATLAB/SIMULINK software under sudden weather conditions changes. Results show accurate and fast response of the converter and inverter control and which leads to fast maximum power point tracking.