DC-DC converters are widely employed to interface one voltage level with another through step-up or step-down operation. In recent years, step-up DC-DC converters have been a key component in harnessing energy through renewable sources by providing an interface to integrate low voltage systems to DC-AC converters or microgrids. They find increasing applications in battery and fuel cell electric vehicles which can benefit from high and variable DC link voltage. It is important to optimize these converters for higher efficiency while achieving high gain and high power density. Non-isolated DC-DC converters are an attractive option due to the reduced complexity of magnetic design, smaller size, and lower cost. However, in these topologies, achieving a very high gain along with high efficiency has been a challenge. This work encompasses different non-isolated high gain DC-DC converters for electric vehicle and renewable energy applications. The converter topologies proposed in this work can easily achieve a conversion ratio above 20 with lower voltage and current stress across devices. For applications requiring wide input or output voltage range, different control schemes, as well as modified converter configurations, are proposed. Moreover, the converter performance is optimized by employing wide band-gap devices-based hardware prototypes. It enables higher switching frequency operation with lower switching losses. In recent times, multiple soft-switching techniques have been introduced which enable higher switching frequency operation by minimizing the switching loss. This work also discusses different soft-switching mechanisms for the high conversion ratio converter and the proposed mechanism improves the converter efficiency significantly while reducing the inductor size. Further, a novel electric vehicle traction architecture with low voltage battery and multi-input high gain DC-DC converter is introduced in this work. The proposed architecture with multiple 48 V battery packs and integrated, multi-input, high conversion ratio DC-DC converters, can reduce the maximum voltage in the vehicle during emergencies to 48 V, mitigate cell balancing issues in battery, and provide a wide variable DC link voltage. The implementation of high conversion ratio converter in multiple configurations for the proposed architecture has been discussed in detail and the proposed converter operation is validated experimentally through a scaled hardware prototype.

Large number of renewable energy based distributed energy resources(DERs) are integrated into the conventional power grid using power electronic interfaces. This causes increased need for efficient power conversion, advanced control, and DER situational awareness. In case of photovoltaic(PV) grid integration, power is processed in two stages, namely DC-DC and DC-AC. In this work, two novel soft-switching schemes for quadratic boost DC-DC converters are proposed for PV microinverter application. Both the schemes allow the converter to operate at higher switching frequency, reducing the converter size while still maintaining high power conversion efficiency. Further, to analyze the impact of high penetration DERs on the power system a real-time simulation platform has been developed in this work. A real, large distribution feeder with more than 8000 buses is considered for investigation. The practical challenges in the implementation of a real-time simulation (such as number of buses, simulation time step, and computational burden) and the corresponding solutions are discussed. The feeder under study has a large number of DERs leading to more than 200% instantaneous PV penetration. Opal-RT ePHASORSIM model of the distribution feeder and different types of DER models are discussed in detailed in this work. A novel DER-Edge-Cloud based three-level architecture is proposed for achieving solar situational awareness for the system operators and for real-time control of DERs. This is accomplished using a network of customized edge-intelligent-devices(EIDs) and end-to-end solar energy optimization platform(eSEOP). The proposed architecture attains superior data resolution, data transfer rate and low latency for the end-to-end communication. An advanced PV string inverter with control and communication capabilities exceeding those of state-of-the-art, commercial inverters has been developed to demonstrate the proposed real-time control. A power-hardware-in-loop(PHIL) and EID-in-loop(EIL) testbeds are developed to verify the impact of large number of controllable DERs on the distribution system under different operational modes such as volt-VAr, constant reactive power and constant power factor. Edge level data analytics and intelligent controls such as autonomous reactive power allocation strategy are implemented using EIL testbed for real-time monitoring and control. Finally, virtual oscillator control(VOC) for grid forming inverters and its operation under different X/R conditions are explored.

The performance of voltage source inverter (VSI) in terms of output waveform quality, conversion efficiency and common mode noise depends greatly on the pulse width modulation (PWM) method. In this work, a low-loss space vector PWM i.e., 240°-clamped space vector PWM (240CPWM) is proposed to improve the performance of VSIs in electric/hybrid electric vehicles (EV/HEVs) and grid connected photovoltaic (PV) systems. The salient features of 240CPWM include 240° clamping of each phase pole to positive or negative DC bus in a fundamental cycle ensuring that switching losses are reduced by a factor of seven as compared to conventional space vector PWM (CSVPWM) at unity power factor. Zero states are completely eliminated and only two nearest active states are used ensuring that there is no penalty in terms of total harmonic distortion (THD) in line current. The THD of the line current is analyzed using the notion of stator flux ripple and compared with conventional and discontinuous PWM method. Discontinuous PWM methods achieve switching loss reduction at the expense of higher THD while 240CPWM achieves a much greater loss reduction without impacting the THD. The analysis and performance of 240CPWM are validated on a 10 kW two-stage experimental prototype. Common mode voltage (CMV) and leakage current characteristics of 240CPWM are analyzed in detail. It is shown analytically that 240CPWM reduces the CMV and leakage current as compared to other PWM methods while simultaneously reducing the switching loss and THD. Experimental results from a 10-kW hardware prototype conform to the analytical discussions and validate the superior performance of 240CPWM. 240CPWM requires a six-pulse dynamic DC link voltage that introduces low frequency harmonics in DC input current and/or AC line currents that can affect maximum power point tracking, battery life or THD in line current. Four topologies have been proposed to minimize the low frequency harmonics in input and line currents in grid-connected PV system with 240CPWM. In order to achieve further benefits in terms of THD and device stress reduction, 240CPWM is extended to three-level inverters. The performance metrics such as THD and switching loss for 240CPWM are analyzed in three-level inverter.

Voltage Source Inverter (VSI) is an integral component that converts DC voltage to AC voltage suitable for driving the electric motor in Electric Vehicles/Hybrid Electric Vehicles (EVs/HEVs) and integration with electric grid in grid-connected photovoltaic (PV) converter. Performance of VSI is significantly impacted by the type of Pulse Width Modulation (PWM) method used.In this work, a new PWM method called 240° Clamped Space Vector PWM (240CPWM) is studied extensively. 240CPWM method has the major advantages of clamping a phase to the positive or negative rail for 240° in a fundamental period, clamping of two phases simultaneously at any given instant, and use of only active states, completely eliminating the zero states. These characteristics lead to a significant reduction in switching losses of the inverter and lower DC link capacitor current stress as compared to Conventional Space Vector PWM. A unique six pulse dynamically varying DC link voltage is required for 240CPWM instead of constant DC link voltage to maintain sinusoidal output voltage. Voltage mode control of DC-DC stage with Smith predictor is developed for shaping the dynamic DC link voltage that meets the requirements for fast control. Experimental results from a 10 kW hardware prototype with 10 kHz switching frequency validate the superior performance of 240CPWM in EV/HEV traction inverters focusing on loss reduction and DC link capacitor currents. Full load efficiency with the proposed 240CPWM for the DC-AC stage even with conventional Silicon devices exceeds 99%. Performance of 240CPWM is evaluated in three phase grid-connected PV converter. It is verified experimentally that 240CPWM performs well under adverse grid conditions like sag/swell and unbalance in grid voltages, and under a wide range of power factor. Undesired low frequency harmonics in inverter currents are minimized using the Harmonic Compensator that results in Total Harmonic Distortion (THD) of 3.5% with 240CPWM in compliance with grid interconnection standards. A new, combined performance index is proposed to compare the performance of different PWM schemes in terms of switching loss, THD, DC link current stress, Common Mode Voltage and leakage current. 240CPWM achieves the best value for this index among the PWM methods studied.

The high uncertainty of renewables introduces more dynamics to power systems. The conventional way of monitoring and controlling power systems is no longer reliable. New strategies are needed to ensure the stability and reliability of power systems. This work aims to assess the use of machine learning methods in analyzing data from renewable integrated power systems to aid the decisionmaking of electricity market participants. Specifically, the work studies the cases of electricity price forecast, solar panel detection, and how to constrain the machine learning methods to obey domain knowledge.Chapter 2 proposes to diversify the data source to ensure a more accurate electricity price forecast. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules: the mapping between the historical wind power and the historical price and the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the learned mapping between power and price. The massive numerical comparison gives guidance for choosing proper machine learning methods and proves the effectiveness of the proposed method. Chapter 3 proposes to integrate advanced data compression techniques into machine learning algorithms to either improve the predicting accuracy or accelerate the computation speed. New semi-supervised learning and one-class classification methods are proposed based on autoencoders to compress the data while refining the nonlinear data representation of human behavior and solar behavior. The numerical results show robust detection accuracy, laying down the foundation for managing distributed energy resources in distribution grids. Guidance is also provided to determine the proper machine learning methods for the solar detection problem. Chapter 4 proposes to integrate different types of domain knowledge-based constraints into basic neural networks to guide the model selection and enhance interpretability. A hybrid model is proposed to penalize derivatives and alter the structure to improve the performance of a neural network. We verify the performance improvement of introducing prior knowledge-based constraints on both synthetic and real data sets.

Wide-BandGap (WBG) material-based switching devices such as gallium nitride (GaN) High Electron Mobility Transistors (HEMTs) and Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) are considered very promising and valuable candidates for replacing conventional Silicon (Si) MOSFETs in various industrial high-frequency high-power applications, mainly because of their capabilities of higher switching frequencies with less switching and conduction losses. However, to make the most of their advantages, it is crucial to understand the intrinsic differences between WBG-based and Si-based switching devices and investigate effective means to safely, efficiently, and reliably utilize the WBG devices. Firstly, a comprehensive understanding of traditional Modular Multilevel Converter (MMC) topology is presented. Different novel SubModule (SM) topologies are described in detail. The low frequency SM voltage fluctuation problem is also discussed. Based on the analysis, some novel topologies which manage to damp or eliminate the voltage ripple are illustrated in detail. As demonstrated, simulation results of these proposed topologies verify the theory. Moreover, the hardware design considerations of traditional MMC platform are discussed. Based on these, a 6 kW smart Modular Isolated Multilevel Converter (MIMC) with symmetrical resonant converter based Ripple current elimination channels is delivered and related experimental results further verify the effectiveness of proposed topology. Secondly, the evolution of GaN transistor structure, from classical normally-on device to normally-off GaN, is well-described. As the benefits, channel current capability and drain-source voltage are significantly boosted. However, accompanying the evolution of GaN devices, the dynamic on-resistance issue is one of the urgent problems to be solved since it strongly affects the GaN device current and voltage limit. Unlike traditional methods from the perspective of transistor structure, this report proposes a novel Multi-Level-Voltage-Output gate drive circuit (MVO-GD) aimed at alleviating the dynamic on-resistance issue from engineering point of view. The comparative tests of proposed MVO-GD and the standard 2-level gate driver (STD-GD) are conducted under variable test conditions which may affect dynamic on-resistance, such as drain-source voltage, gate current width, device package temperature and so on. The experimental waveforms and data have been demonstrated and analyzed.

Voltage Source Converters (VSCs) have been widely used in grid-connected applications with Distributed Energy Resource (DER) and Electric Vehicle (EV) applications. Replacement of traditional thyristors with Silicon/Silicon-Carbide based active switches provides full control capability to the converters and allows bidirectional power flow between the source and active loads. In this study, advanced control strategies for DER inverters and EV traction inverters will be explored.Chapter 1 gives a brief introduction to State-of-the-Art of VSC control strategies and summarizes the existing challenges in different applications. Chapter 2 presents multiple advanced control strategies of grid-connected DER inverters. Various grid support functions have been implemented in simulations and hardware experiments under both normal and abnormal operating conditions. Chapter 3 proposes an automated design and optimization process of a robust H-infinity controller to address the stability issue of grid-connected inverters caused by grid impedance variation. The principle of the controller synthesis is to select appropriate weighting functions to shape the systems closed-loop transfer function and to achieve robust stability and robust performance. An optimal controller will be selected by using a 2-Dimensional Pareto Front. Chapter 4 proposes a high-performance 4-layer communication architecture to facilitate the control of a large distribution network with high Photovoltaic (PV) penetration. Multiple strategies have been implemented to address the challenges of coordination between communication and system control and between different communication protocols, which leads to a boost in the communication efficiency and makes the architecture highly scalable, adaptive, and robust. Chapter 5 presents the control strategies of a traditional Modular Multilevel Converter (MMC) and a novel Modular Isolated Multilevel Converter (MIMC) in grid-connected and variable speed drive applications. The proposed MIMC is able to achieve great size reduction for the submodule capacitors since the fundamental and double-line frequency voltage ripple has been cancelled. Chapter 6 shows a detailed hardware and controller design for a 48 V Belt-driven Starter Generator (BSG) inverter using automotive gate driver ICs and microcontroller. The inverter prototype has reached a power density of 333 W/inch3, up to 200 A phase current and 600 Hz output frequency.

This paper introduces an application space of Power over Ethernet to Universal Serial Bus (USB) Power Delivery, and develops 3 different flyback approaches to a 45 Watt solution in the space. The designs of Fixed Frequency Flyback, Quasi-Resonant Flyback, and Active Clamp Flyback are developed for the application with 37 Volts (V) to 57 V Direct Current (DC) input voltage and 5 V, 9 V, 15 V, and 20 V output, and results are examined for the given specifications. Implementation based concerns are addressed for each topology during the design process. The systems are proven and tested for efficiency, thermals, and output voltage ripple across the operation range. The topologies are then compared for a cost and benefit analysis and their highlights are identified to showcase each systems prowess.

With the rapid advancement in the technologies related to renewable energies such

as solar, wind, fuel cell, and many more, there is a definite need for new power con

verting methods involving data-driven methodology. Having adequate information is

crucial for any innovative ideas to fructify; accordingly, moving away from traditional

methodologies is the most practical way of giving birth to new ideas. While working

on a DC-DC buck converter, the input voltages considered for running the simulations

are varied for research purposes. The critical aspect of the new data-driven method

ology is to propose a machine learning algorithm. In this design, solving for inductor

value and power switching losses, the parameters can be achieved while keeping the

input and output ratio close to the value as necessary. Thus, implementing machine

learning algorithms with the traditional design of a non-isolated buck converter deter

mines the optimal outcome for the inductor value and power loss, which is achieved

by assimilating a DC-DC converter and data-driven methodology.

The present thesis investigates the different outcomes from machine learning al

gorithms in comparison with the dynamic equations. Specifically, the DC-DC buck

converter will be focused on the thesis. In order to determine the most effective way

of keeping the system in a steady-state, different circuit buck converter with different

parameters have been performed.

At present, artificial intelligence plays a vital role in power system control and

theory. Consequently, in this thesis, the approximation error estimation has been

analyzed in a DC-DC buck converter model, with specific consideration of machine

learning algorithms tools that can help detect and calculate the difference in terms

of error. These tools, called models, are used to analyze the collected data. In the

present thesis, a focus on such models as K-nearest neighbors (K-NN), specifically

the Weighted-nearest neighbor (WKNN), is utilized for machine learning algorithm

purposes. The machine learning concept introduced in the present thesis lays down

the foundation for future research in this area so that to enable further research on

efficient ways to improve power electronic devices with reduced power switching losses

and optimal inductor values.

This dissertation covers three primary topics and relates them in context. High frequency transformer design, microgrid modeling and control, and converter design as it pertains to the other topics are each investigated, establishing a summary of the state-of-the-art at the intersection of the three as a baseline. The culminating work produced by the confluence of these topics is a novel modular solid-state transformer (SST) design, featuring an array of dual active bridge (DAB) converters, each of which contains an optimized high-frequency transformer, and an array of grid-forming inverters (GFI) suitable for centralized control in a microgrid environment. While no hardware was produced for this design, detailed modeling and simulation has been completed, and results are contextualized by rigorous analysis and comparison with results from published literature. The main contributions to each topic are best presented by topic area. For transformers, contributions include collation and presentation of the best-known methods of minimum loss high-frequency transformer design and analysis, descriptions of the implementation of these methods into a unified design script as well as access to an example of such a script, and the derivation and presentation of novel tools for analysis of multi-winding and multi-frequency transformers. For microgrid modeling and control, contributions include the modeling and simulation validation of the GFI and SST designs via state space modeling in a multi-scale simulation framework, as well as demonstration of stable and effective participation of these models in a centralized control scheme under phase imbalance. For converters, the SST design, analysis, and simulation are the primary contributions, though several novel derivations and analysis tools are also presented for the asymmetric half bridge and DAB.