This thesis describes a very high-power density inverter design for a power train that is intended to be used in an all-electric aircraft. The minimum power density for the power electronics for such an aircraft is 50 kW/kg, but this thesis details designs reaching 60 to even 70 kW/kg, even for a multilevel topology inverter. SiC devices were explored in this thesis because wider bandgap materials such as SiC and GaN can operate at higher temperatures, higher power densities, higher voltages, and higher frequencies – making it the most optimal choice for a long-range all-electric aircraft. This thesis also explored numerous multi-inverter topologies to find the most optimal one for the application – ranging from the typical half-bridge to a neutral-point clamped to even a flying capacitor inverter. Thermal calculations were made only for air-cooling; however, future designs aim for liquid cooling for a longer life-time design. Mechanical bus bar work for a prototype and future designs were made on Onshape and presented here. Additional improvements were explored, ranging from incorporating liquid cooling to an extension to soft-switching. A single-phase prototype was made and experimented. The results are presented in this paper. Currently, I am working on improving the prototype with a next iteration of the design, eventually incorporating it with the motor and thermal designs from other projects.

Extensive deployment of power electronic loads in naval ship power systems indicate full ship electrification is inevitable. Next generation warships require high power density weapons drawing pulse power from a dc power grid. A particularly concerning issue is that these pulse loads draw large currents in short periods of time and are similar in behavior to a fault; and therefore may be indiscernible from a fault. This dissertation introduces novel machine/deep learning based algorithms, including long short-term memory recurrent neural network based autoencoders and data-driven clustering based machine learning approaches to detect dc faults and monitor load conditions applied to naval pulse loads. Two feature extraction methods are also implemented including the short-time Fourier transform and stationary wavelet transform. The novel load monitoring solution presented herein can be applied to any load profile that exhibits repetitive transients during normal operation. The frequency-domain features of the load current are extracted for the network training to set the network weights and biases. Once the network training is completed, the machine/deep learning approach will predict both signal classification and fault identification. Finally, the method is demonstrated in a low power laboratory system meant to mimic naval shipboard power systems.

Recently there has been growing interest in multi-phase motors and drives due to their potential advantages including lower torque ripple and harmonic currents, better power density, higher reliability and stability. Two commonly used modulation methods space-vector modulation and sine-triangle modulation are proposed for using in multi-phase induction motors. In this paper, the implementation details of producing random switching patterns are presented by using MATLAB/Simulink. The simulation results are shown for three-level five-phase induction motor. Add current harmonics in the sine-triangle modulation based on the result of SVM. Finally, compare their torque ripples and show the further optimized scheme.

This thesis explores load monitoring on dc micro-grids specifically applied to Naval shipboard power systems. More electronic loads are being placed on Naval ships and, increasingly, more of these loads are pulsed-power in nature; drawing pulsating current from the grid. This presents a challenge for conventional fault monitoring devices as many fault currents are also pulsating in nature and can be difficult to differentiate from a desirable pulse event. Short-time Fourier transform is the technique preferred in this work for spectral analysis of the current signals. In addition to event based monitoring, another unsupervised control is being employed to continuously check the frequency content of the current to look for arcing faults caused by loose electrical connections. The objective of the dissertation is to develop a load monitoring algorithm that records the current drawn by these loads and is able to detect events and differentiate between desirable events and faults using their frequency content in run-time. The algorithm is realized on an micro-controller unit and validated on a low-voltage dc test-bed.