High-frequency wireless power transfer (WPT) technology provides superior compatibility in the alignment with various WPT standards. However, high-efficiency and compact single-phase power switching systems with ideal snubber circuits are required for maximum power transfer capability. This research aims to develop an inverter using Gallium Nitride (GaN) power transistors, optimized RCD (resistor/capacitor/diode) snubber circuits, and gate drivers, each benefitting WPT technology by reducing the switching and conduction loss in charging electric vehicle batteries. A full-bridge GaN inverter was simulated and instituted as part of the wireless charging circuit design. The RCD circuits were adjusted by transferring maximum power from the power supply to the transmitter inductor. For verification of the simulated output, lab-scale experiments were implemented for two half-bridges controlled by gate drivers with corresponding snubber circuits. After authenticating the output results, the GaN inverter was tested with an input range of 30 V to deduce the success of charging electric vehicle batteries within an efficient time frame. The developed inverter, at 80 kHz frequency, was applied in place of a ready-to-use evaluation board, fully reducing less harmonic distortion and greatly increasing WPT system efficiency (~93%). In turn, the designed GaN inverter boasts considerable energy savings, resulting in a more cost-effective solution for manufacturers.

Higher operational requirements in cyber–physical microgrid system stress the electrical system and may push it to the edge of stability. Therefore, prognosis of the imminent failures is vital. Accessing stray electromagnetic waves of power components helps in power system protection and non‐intrusive prognosis of electric components faults in a cyber–physical microgrid environment. This study implements a cyber–physical approach associated between the electromagnetic waves radiated by components in the microgrid and the communication structure. To verify the same, the entire system is implemented on a real‐time lab‐based microgrid environment. The major problem with the stray electromagnetic waves is receiving appropriate fields. This is resolved by placing magnetic coil antennas at optimal distances and monitoring the radiated electromagnetic waves and their harmonics. Quick response code recognition technique is used to recognise the source and its corresponding healthy mode while harmonic analysis through artificial neural network helps to find the type and origin of faults. This would be an artificial intelligence‐enabled system which self‐optimises and acts according to the patterns. The proposed monitoring system can be utilised in any cyber–physical microgrid system especially those located in extreme/remote areas.

This paper presents a hybrid model–based control algorithm that combines Model Predictive Control (MPC) and Sequential Action Control (SAC) deployed in a high-speed drive for Brushless DC (BLDC) motor by using a DC-DC converter with Gallium Nitride (GaN) switches. GaN FETs are selected because of their higher speed and lower power loss as compared with traditional Si switches. In the proposed framework, SAC processes the initial values of the control variables as well as their time of application and their duration in MPC loop. After receiving the underlying estimation of future contribution from SAC, MPC consolidates it with current input and predicts future control values by using the system state space model. This hybrid control conserves control effort and reduces sensitivity to initial conditions. In this way, converter’s output voltage is controlled to produce the reference speed at the motor output. National Instrument PXIe-6356 module is utilized as the interface between software and hardware that is a multi-function, LabVIEW-compatible data acquisition device. The viability of the proposed hybrid optimization for the high-speed drive is confirmed numerically by utilizing MATLAB/Simulink and approved experimentally using a Gallium Nitride (GaN) half-bridge DC-DC converter.