Executive Summary : | Global energy demand is increasing, driving the need for sustainable solutions. The 2030 for Sustainable Development agenda focuses on renewable energy and reducing global warming. Power electronic converters play a crucial role in incorporating renewable energy into the grid. However, fast-switching operation and non-linear characteristics can generate harmonic pollution, affecting power quality. A new term, supraharmonics, is also affecting modern power systems. This proposal aims to analyze supraharmonics from EV charging stations and integrate renewable energy sources into distribution systems using artificial techniques. The findings will help promote sustainable energy goals, minimize environmental consequences, and improve power grid dependability. Research gaps exist in measuring and analyzing supraharmonic emissions, with limited focus on source identification. A novel approach combines machine learning methods with modern signal processing techniques to detect and categorize emissions. An AI-powered system will be developed to detect and mitigate supraharmonics in the early stages. The study aims to design an EV charging station with various charging levels and solar and wind energy conversion systems, analyzing output current and voltage using MATLAB software. The results will be used to train an AI algorithm for identifying the spectrum of supraharmonics. |