Grid-Connected RES Integration for Power Optimization Using Multi-Level Inverters and AI Techniques

Abstract

Interest in renewable energy is growing in every corner of the world. Most renewable power, by definition, produces energy on a temporary basis; the integrations of renewable energies into electric grids are the challenging task, as renewable power is often intermittent. Transceivers play an important role in measuring, operations, and security of renewables producers and power grids, making them a vital piece of technology in this link to the grid. In order to get the most out of energy converters installed in three-phase, four-wire distributors, and this article proposes a novel control method for energy converters using Convolutional Neural Networks (CNNs). CNNs are the types of artificial intelligence that can be used to learn complex relationships between input and output data. In this case, the input data is the state of the inverter and the output data is the control signals. It used to be governing inverters so that they can perform the duties of a multifunctional device Simulation studies in Simulink and MATLAB are used to demonstrate this novel control paradigm, and the experimental data from lab work with DSPs provides further confirmation.