Fault-Tolerant High-Density Power Converters and In-Situ Health Prediction for Offshore MVDC Distribution

A fault-tolerant solid-state transformer (SST) structure to combine the benefits of higher power density and robustness in medium-voltage DC (MVDC) electric distribution systems is proposed in this dissertation. A SiC-MOSFET-based 6 MW, 36/6 kV ISOSP (input series output series-parallel) modular stacked DC/DC SST is proposed using medium frequency (MF) transformer isolation. This structure renders the system with fault tolerance and the capability to operate normally even in a partial fault condition. Small-signal modeling, simulations, and Typhoon-HiL real-time system were performed to verify the operation of the converter. Experimental results from a scaled-down laboratory prototype prove the feasibility of the proposed isolated DC/DC structure and control system. While replacing a low-frequency transformer (LFT) with an SST for a certain application, the design of the transformer must be primarily optimized for size and efficiency. In this work, the transient model of a single-phase E-core transformer using a Multi-Turn Coil Domain was used to analyze the electromagnetic field. A medium frequency (MF) transformer with a ferrite core is designed and simulated in COMSOL© based on hardware prototype specifications, and outcomes from 3-D finite element analysis (FEA) matched the 20 kHz MF transformer design used in the hardware. The model includes the analysis of the nonlinear B-H curve, including saturation effects in the core to simulate the magnetic behavior of the soft-iron core. A pulsating voltage, including with up to 7th harmonic, to simulate the effects of a near-square wave is applied to the model. The FEA design was done before manufacturing and to confirm the behavior of the designed transformer, especially for peak flux density. It was also possible to compare the volume of the proposed MF transformer with LF transformers using FEA simulation. Overall, the proposed system can lead to significant improvement in power density in mission-critical MVDC applications such as subsea electrification. The reliability prediction and survival indices calculation are required for the sensitive operation of the proposed fault-tolerant converter. A better understanding of the failure mechanisms and barriers to the utilization of electronic devices in extreme environments leads to reliable power converters in offshore applications. It is well known that each component's reliability in a power converter affects the reliability of the overall system. Due to the advancements in computing infrastructure and sensor technologies, data-driven approaches for predicting the health of power converters in real-time are slowly becoming popular. This research proposes a new statistical approach using probability density functions (PDFs) and associated concepts in measure theory to predict the probability of system failure using individual components’ degradation data. For this purpose, remaining-useful-life (RUL) is estimated for each power component (or sub-system) using qualification data, followed by an evaluation of a cumulative probability of survival for the converter. An artificial neural network (ANN) is then trained to quickly estimate in real-time the probability of survival of the power converter in the future. While the algorithm involves multiple computation steps, the RUL prediction accuracy using the proposed method will be high due to the data-driven approach. Moreover, the machine learning-based model resulting from this approach to predict the probability of survival is low on memory utilization. It is envisioned that this approach can be used to create digital twins of power converters in practical circuits, optimize performance, and predict RUL. This dissertation explains the theory followed by scaled-down hardware of an isolated modular DC-DC converter. An experimental qualification setup for device degradation test and system-level RUL measurement methods are provided.