PV-based Self-Lift DC-DC Converter for Grid Integration Using Advanced Control Technique

The growing demand for Renewable Energy Sources (RES) has propelled the development of efficient photovoltaic (PV) systems for grid integration. This approach aims to maximize energy extraction while ensuring stable and efficient connection to the electrical grid. This research develops An method to improving efficiency of PV grid connections through the integration of Genetic Algorithm Adaptive Neuro-Fuzzy Inference System (GA-ANFIS)-based Maximum Power Point Tracking (MPPT) and Proportional-Integral (PI) control techniques. The proposed system utilizes a self-lift Luo converter to optimize voltage output from the PV panels, facilitating optimal power retrieval in different environmental issues. The generated signals are processed through a single-phase voltage source inverter (VSI) and an LC filter, which facilitate smooth integration into the single-phase grid. The GA-ANFIS MPPT algorithm dynamically handles the process of the PV system, while the PI controller manages reactive energy flow, ensuring stability and reliability of the grid connection. In this system, improved performance metrics, including enhanced power efficiency and reduced harmonic distortion. The proposed system is implemented in MATLAB, utilizing its powerful simulation capabilities to analyze the PV system. This simulation framework allows for the testing and validation of key performance metrics, such as 95% efficiency and 1.9% Total Harmonic Distortion (THD), demonstrating an enhancing power extraction and grid stability. This innovation demonstrates the promise of modern control approaches in maximizing renewable energy systems, hence promoting the wider adoption of sustainable energy solutions.