Neural network approaches for enhancing near-field wireless power transfer

This paper looks at new developments in near-field wireless power transfer using a fresh method that aims to improve stability, efficiency, and long-term viability while testing and developing. Magnetic induction and resonant coupling are two common ways to send power over short distances, but they aren't always reliable or efficient. To solve these problems, we offer a full system that includes up-to-date signal processing techniques, the best antenna designs, and effective ways to reduce clutter. The study carefully compares the suggested method to well-known standards for things like power transfer efficiency, alignment errors, frequency stability, energy conversion efficiency, system dependability, and real-time adaptability. A study comparing different methods shows that the suggested method is more reliable and efficient, and it also lowers the number of alignment mistakes and running costs compared to other methods. The study also looks at problems of environmental impact and regulatory compliance, showing that the method meets both legal standards and has a beneficial effect on the environment. These features are crucial for making sure that wireless power transfer systems are legal and safe for the environment. The work includes an ablation study to split the roles of different parts in the suggested approach. This shows how important signal processing methods, antenna designs, and interference-reducing methods are for improving performance.