Abstract
Due to the large demand on energy, energy sources, as well as the problems of the environment such as the dynamic weather conditions. Hence the world researchers nowadays are moving toward using solar energy because it gives different advantages over the traditional energy sources such as low maintenance costs, eternal sun energy, and the lack of revival of the gases of green houses. As a result, the photo- voltaic (PV) systems' power will be reduced. Under different weather conditions, maximizing the power point tracking (MPPT) is an important part to improve the solar systems power. In this paper, we introduce the neural network approaches for the PV systems. This paper also presents a novel application of Fuzzy Neural Network (FNN) in modeling a PV. The photovoltaic system model is designed with the use of MATLAB/SIMULINK software program with the connection of a DC-DC boost converter, a Maximum Power Point Tracking (MPPT) controller, a one-phase Voltage Source Converter (VSC) and a three-level bridge. The MPPT controller is used to cover the need for advanced controller that can detect the maximum power point in solar cell systems that have unstable current and voltage and keep the resultant power per cost low.