Maximizing Solar PV Performance with Deep Learning Optimization and Advanced Power Regulation

Abstract

This study presents a two-stage; three-phase grid-connected solar photovoltaic (PV) system that leverages advanced deep learning techniques to optimize Maximum Power Point Tracking (MPPT), significantly enhancing energy extraction and power quality. The system architecture integrates a Landsman Converter with an Artificial Neural Network (ANN)-based MPPT controller, which dynamically adjusts the duty cycle to respond to fluctuations in irradiance and temperature, ensuring efficient DC-DC conversion. Building on this, a deep learning layer continuously refines the MPPT algorithm in real time, boosting tracking accuracy and response speed. In the first stage, the Landsman Converter, optimized with deep learning, enables rapid, precise power tracking, achieving an impressive MPPT efficiency of 86.38%. The second stage incorporates a Phase-Locked Loop (PLL)-controlled DC-AC inverter, providing stable grid synchronization and reducing Total Harmonic Distortion (THD) to -40.27%, which enhances overall power quality and supports grid stability under variable conditions. MATLAB simulations demonstrate that this deep learning-enhanced system consistently outperforms conventional approaches, establishing its potential as a scalable, high-efficiency solution for grid-connected PV systems. The proposed framework not only improves real-time energy yield but also supports stable renewable energy integration into the grid, promoting sustainable energy adoption. By combining deep learning with traditional ANN-based MPPT in a robust, two-stage control system, this research offers a promising approach to maximizing solar PV performance, making a valuable contribution to the field of renewable energy and intelligent power regulation..