Implementing Machine Learning Techniques to Optimize Near-Field Wireless Power Transfer

With applications ranging from consumer electronics to medical equipment, electric cars, and industrial automation, near-field wireless power transfer, or WPT, has become a promising new technology. This abstract offers a unique method for optimizing near-field WPT using machine learning approaches, which offers notable improvements in user experience, safety, efficiency, and flexibility. Three important machine learning techniques are introduced by the suggested method: Dynamic Power Control using Neural Networks (DPC-NN), Predictive User Behavior Modeling (PUBM), and Reinforcement Learning for Resonance Tuning (RLRT). To guarantee the best possible power transfer efficiency, RLRT continually adjusts the system's resonance frequency. By predicting user behavior, PUBM helps the system be ready for charging events in advance. To ensure safe and effective operation, DPC-NN dynamically regulates power supply. A glaring benefit may be shown when comparing the suggested approach with six conventional approaches. In terms of effectiveness, security, user happiness, interference reduction, predictive maintenance, dependability, flexibility, and environmental impact, the suggested approach continuously performs better. These findings demonstrate how machine learning may revolutionize near-field WPT and spur efficiency and innovation in a range of sectors.