Search

In this article, the effect of the dead-time on a single-phase wireless power transfer (WPT) system is studied in detail. In practice, the dead-time is always placed between the complementary switching pulses of the inverter phase-leg. At higher operating frequencies, the dead-time issues in the resonant inverter become critical, especially as the power level increases. The detailed analysis of the dead-time on a WPT system is discussed for different operating conditions of the inverter duty-cycle and power factor. The switching characteristics of the WPT system inverter are analyzed, and the notch phenomenon that appears at the output of the inverter is also discussed. A notch equation based on the observations is derived to predict the notch occurrence during the system operation. Furthermore, the mathematical expressions are presented for different notch conditions. Subsequently, the effect of the notches on the sensitivity and the power transfer of the series–series compensated WPT system is analyzed. Finally, the approach is verified experimentally on an 8-kW WPT system prototype, and the results are compared with the theoretical analysis. [ABSTRACT FROM AUTHOR]

This article presents a systematic power stage design approach for a high-power density air-cooled inverter, which involves the utilization of emerging 1.7 kV silicon carbide (SiC) mosfet bare die engineering samples, heatsinks optimized with genetic algorithm, and built using three-dimensional printing technology and integrated power modules with a novel packaging structure. The developed air-cooled inverter assembly is mainly composed of the SiC mosfet phase leg modules with split high-side and low-side switch submodules, which are attached to two separate heatsinks for increased heat dissipation area and reduced thermal resistance. The heatsink is designed using a co-simulation environment with finite element analysis in COMSOL and genetic algorithm in MATLAB. The primary design procedure, including bare die device characterization, loss calculation, thermal evaluation, and power module development, is elaborated. The proposed design approach is verified and validated through experiments at each stage of development. The experimental results show that the inverter California Energy Commission efficiency is 98.4%, and a power density of 75 W/in3 is achieved with a sufficient junction temperature margin for semiconductor long-term reliability. [ABSTRACT FROM AUTHOR]

A new approach for modulation of an 11-level cascade multilevel inverter using selective harmonic elimination is presented in this paper. The dc sources feeding the multilevel inverter are considered to be varying in time, and the switching angles are adapted to the dc source variation. This method uses genetic algorithms to obtain switching angles offline for different dc source values. Then, artificial neural networks are used to determine the switching angles that correspond to the real-time values of the dc sources for each phase. This implies that each one of the dc sources of this topology can have different values at any time, but the output fundamental voltage will stay constant and the harmonic content will still meet the specifications. The modulating switching angles are updated at each cycle of the output fundamental voltage. This paper gives details on the method in addition to simulation and experimental results. [ABSTRACT FROM PUBLISHER]

This work approximates the selective harmonic elimination problem using artificial neural networks (ANNs) to generate the switching angles in an 11-level full-bridge cascade inverter powered by five varying dc input sources. Each of the five full bridges of the cascade inverter was connected to a separate 195-W solar panel. The angles were chosen such that the fundamental was kept constant and the low-order harmonics were minimized or eliminated. A nondeterministic method is used to solve the system for the angles and to obtain the data set for the ANN training. The method also provides a set of acceptable solutions in the space where solutions do not exist by analytical methods. The trained ANN is a suitable tool that brings a small generalization effect on the angles' precision and is able to perform in real time (50-/60-Hz time window). [ABSTRACT FROM AUTHOR]