ENHANCING PHOTOVOLTAIC SYSTEM PERFORMANCE THROUGH A MULTI-LEVEL BOOST-BASED NEURAL NETWORK OPTIMIZATION

Abstract

To get better the performance of the photovoltaic (PV) system, in other words, to maximize the power delivered to the charge connected to the terminals of the PV generator, several methods of optimization have been applied, and approaches have been followed to good adaptation and high competence. Among these means, is the use of an intelligent multi-level cutter. Multi-level boost converter structures have brought more undeniable continuous conversion, especially in high-power applications. Furthermore, this article underscores the importance of modeling and control using neural networks (NNs) to optimize the operation of the solar conversion chain. NNs are employed to analyze system data in real time and adjust operating parameters to maximize solar conversion efficiency. This approach enables dynamic adaptation to variations in weather conditions and load, ensuring optimal operation of the PV system in various situations.