Design and Simulation of an Intelligent Grid-Connected MPPT Inverter with Battery Storage Using ANN Algorithm

When electrical demand is rapidly increasing, renewable energy sources are crucial for maintaining the electricity of the grid and powering disconnected loads. Photovoltaic array output is nonlinear and varies with sun irradiation and cell temperature. As a result, a Maximum Power Point Tracking (MPPT) approach is required to extract peak power from the solar array to optimize the produced energy. This research delves into the concept of MPPT technologies, which significantly improve the efficiency of a solar PV system. An MPPT controller based on an artificial neural network has been presented. The data for the ANN model are acquired using the perturbation and observation methods. The objective of the implementation of ANN is to extract the MPP regardless of irradiation variation. A boost converter is used to inject power from PV into the grid. An inverter (DC/AC) with filter LC is made a cascade with a boost converter to synchronize the frequency of the grid with the inverter with PID controller and SPWM technique. Lithium-ion batteries are the best solution utilized to stock energy. We control the charge and discharge of the battery by a PID controller, such as using a converter buck-boost. A simulation-based study of the system was provided utilizing the MATLAB/Simulink toolset.