Neural Network Control for Efficient Power Management of Renewable Energy Sources in Microgrids

The use of solar energy with storage system to generate electricity has increased significantly. But due to the high cost of batteries. particularly in, the field of photovoltaic grid-connected systems the increase is still moderate. The main goal of this research is to simulate the performance of grid-connected single-phase, one-stage photovoltaic (PV) systems under various meteorological conditions. Constraints on measuring the system operational efficiency on measuring the systems' operational efficiency. This work focuses on boosting power extraction utilizing extremely effective Maximum Power Point Tracking (MPPT) algorithms. The investigation compares more sophisticated techniques like the Artificial Neural Network algorithm (ANN) with more conventional methods like Incremental Conductance (IncCond) and Perturb and Observe (P&O). In order to determine the ideal filter parameters. It is necessary to build and operate a passively damped LCL filter. Building a connection between an inverter, resistive and inductive loads. and the public utility grid allows the project to investigate alternate methods of controlling active and reactive powers.