The yield from a solar photovoltaic (PV) source is dependent on factors such as light and temperature. A control system called a maximum power point tracker (MPPT) ensures that the yield from a solar PV source is maximized in spite of these factors. This thesis presents a novel implementation of a perturb and observe (PO) MPPT. The implementation uses a switched capacitor step down converter and a custom digital circuit implementation of the PO algorithm. Working in tandem, the switched capacitor step down converter and the custom digital circuit implementation were able to successfully track the maximum power point of a simulated solar PV source. This implementation is free of the overhead encountered with general purpose processor based MPPT implementations. This makes this MPPT system a valid candidate for applications where general purpose processors are undesirable. This document will begin by discussing the current state of MPPT research. Afterward, this thesis will present studies done to be able to use the chosen switched capacitor step down converter. Then the digital circuit PO implementation will be discussed in detail. Simulations of the architecture will be presented. Finally, experimental validation using a hardware prototype will be shown.

This thesis is based on promising computing paradigm of reversible logic which generates unique outputs out of the inputs and. Reversible logic circuits maintain one-to-one mapping inside of the inputs and the outputs. Compared to the traditional irreversible computation, reversible logic circuit has the advantage that it successfully avoids the information loss during computations. Also, reversible logic is useful to design ultra-low-power nanocomputing circuits, circuits for quantum computing, and the nanocircuits that are testable in nature. Reversible computing circuits require the ancilla inputs and the garbage outputs. Ancilla input is the constant input in reversible circuits. Garbage output is the output for maintaining the reversibility of the reversible logic but is not any of the primary inputs nor a useful bit. An efficient reversible circuit will have the minimal number of garbage and ancilla bits. Barrel shifter is one of main computing systems having applications in high speed digital signal processing, oating-point arithmetic, FPGA, and Center Processing Unit (CPU). It can operate the function of shifting or rotation for multiple bits in only one clock cycle. The goal of this thesis is to design barrel shifters based on the reversible computing that are optimized in terms of the number of ancilla and garbage bits. In order to achieve this goal, a new Super Conservative Reversible Logic Gate (SCRL gate) has been used. The SCRL gate has 1 control input depending on the value of which it can swap any two n-1 data inputs. We proved that the SCRL gate is superior to the existing conservative reversible Fredkin gate. This thesis develops 5 design methodologies for reversible barrel shifters using SCRL gates that are primarily optimized with the criteria of the number of ancilla and garbage bits. The five proposed methodologies consist of reversible right rotator, reversible logical right shifter, reversible arithmetic right shifter, reversible universal right shifter and reversible universal bidirectional shifter. The proposed reversible barrel shifter design is compared with the existing works in literature and have shown improvement ranging from 8.5% to 92% by the number of garbage and ancilla bits. The SCRL gate and design methodologies of reversible barrel shifter are mapped in Quantum Dot Cellular Automata (QCA) computing. It is illustrated that the SCRL-based designs of reversible barrel shifters have less QCA cost (cost in terms of number of inverters and majority voters) compared to the Fredkin gate- based designs of reversible barrel shifters.

Cardiac magnetic resonance (MR) imaging can non-invasively assess heart function. Displacement encoding with stimulated echoes (DENSE) is an advanced cardiac MR imaging technique that measures tissue displacement and can be used to quantify cardiac mechanics (e.g. strain and torsion). When combined with clinical risk factors, cardiac mechanics have been shown to be better predictors of mortality than traditional measures of heart function. End-expiratory breath-holds are typically used to minimize respiratory motion artifacts. Unfortunately, requiring subjects to breath-hold introduces limitations with the duration of image acquisition and quality of data acquired, especially in patients with limited ability to hold their breath. Thus, DENSE acquisitions often require respiratory navigator gating, which works by measuring the diaphragm during normal breathing and only acquiring data when the diaphragm is within a pre-defined acceptance window. Unfortunately, navigator gating results in long scan durations due to inconsistent breathing patterns. Also, the navigator echo can be used in different ways to accept or reject image data, which creates several navigator configuration options. Each respiratory navigator configuration has distinct advantages and disadvantages that directly affect scan duration and image quality, which can affect derived cardiac mechanics. Scan duration and image quality need to be optimized to improve the clinical utility of DENSE. Thus, the goal of this project was to optimize those parameters. To accomplish this goal, we set out to complete 3 aims: 1) understand how respiratory gating affects the reproducibility of measures of cardiac mechanics, 2) determine the optimal respiratory navigator configuration, and 3) reduce scan duration by developing and using an interactive videogame to optimize navigator efficiency. Aim 1 of this project demonstrated that the variability in torsion, but not strain, could be significantly reduced through the use of a respiratory navigator compared to traditional breath-holds. Aim 2 demonstrated that, among the configuration options, the dual-navigator configuration resulted in the best image quality compared to the reference standard (traditional breath-holds), but also resulted in the longest scan duration. In Aim 3, we developed an interactive breathing-controlled videogame and demonstrated that its use during cardiac MR can significantly reduce scan duration compared to traditional free-breathing and also led to a small improvement in signal-to-noise ratio of the acquired images. In summary, respiratory navigator gating with DENSE 1) reduces the variability in measured LV torsion, 2) results in the best image quality with the dual-navigator configuration, and 3) results in significantly shorter scan durations through the use of an interactive videogame. Selecting the optimal navigator configuration and using an interactive videogame can improve the clinical utility of DENSE.

Time Delay of Arrival (TDOA) based algorithms and Steered Response Power (SRP) based algorithms are two most commonly used methods for sound source detection and localization. SRP is more robust under high reverberation and multi-target conditions, while TDOA is less computationally intensive. This thesis introduces a modified TDOA algorithm, TDOA delay table search (TDOA-DTS), that has more stable performance than the original TDOA, and requires only 4% of the SRP computation load for a 3-dimensional space of a typical room. A 2-step adaptive thresholding procedure based on a Weibull noise peak distributions for the cross-correlations and a binomial distribution for combing potential peaks over all microphone pairs for the final detection. The first threshold limits the potential target peaks in the microphone pair cross-correlations with a user-defined false-alarm (FA) rates. The initial false-positive peak rate can be set to a higher level than desired for the final FA target rate so that high accuracy is not required of the probability distribution model (where model errors do not impact FA rates as they work for threshold set deep into the tail of the curve). The final FA rate can be lowered to the actual desired value using an M out of N (MON) rule on significant correlation peaks from different microphone pairs associated is a point in the space of interest. The algorithm is tested with simulated and real recorded data to verify resulting FA rates are consistent with the user-defined rates down to 10-6.

In this dissertation, the generalized averaging method models for inverters, reactive power control methods for photovoltaic inverters, and a noise immunity improvement for hybrid position observers for brushless dc motor drives are studied. Models of inverters and other converters based on averaging have been widely used in numerous simulation applications. Generalized averaging can be applied to model both average and switching behavior of converters while retaining the faster run times associated with average-value models. Herein, generalized average models for single- and three-phase pulse width modulation inverters are proposed. The modulation signal for the proposed model could be either a sinusoidal waveform without high order harmonics or a sinusoidal waveform with third-harmonic injection. And this generalized average models also can apply for modeling three-phase pulse width modulation inverters with varying modulation signal frequency in the reference frame. These models are based on a quasi-Fourier series representation of the switching functions that includes fundamental and switching frequency components as well as sideband components of the switching frequency. The proposed models are demonstrated both in simulation and experimentally and are found to accurately portray both the fundamental and the switching behavior of the inverter. In particular, the use of sideband components allows accurate representation of the variation in switching ripple magnitude that occurs in the steady state. The generalized average models are found to have simulation run times that are significantly faster than those associated with detailed models. Therefore, the proposed generalized average models are suitable for simulation applications in which both accuracy (including the switching behavior) and fast run times are required (e.g., long simulation times, systems with multiple converters, and repeated simulations). Variations in the output power of intermittent renewable sources can cause significant fluctuations of distribution system voltage magnitudes. Reactive power control methods that employ the reactive power capability of photovoltaic three-phase inverters to mitigate these fluctuations are proposed. These control methods cause the three-phase inverters to substitute reactive output power for real output power when fluctuations in the solar power are experienced, allowing the fluctuations to be controlled. Performance metrics for assessing the ability of these controllers to perform this mitigation are defined. The controllers are examined using the IEEE 123-bus feeder distribution system, and it is found that the controllers can effectively mitigate voltage magnitude fluctuations and that the appropriate choice of controller depends on the performance metrics of interest. Finally, a noise immunity improvement for hybrid position observers for brushless dc motor drives is proposed. A finite state machine is used to detect Hall-effect sensor transitions to determine if these transitions are true transitions or the result of momentary glitches. This filter causes a delay in the detection of the Hall-effect sensors that is compensated in the proposed observer. The proposed observer is compared in simulations with the original hybrid position observer under both non-noisy and noisy conditions for both constant and variable speed operation, and it has good performance even under high noise and variable speed conditions.

The method of moment (MoM) [1] is a widely used method in electromagnetics to solve static and dynamic electromagnetic problems on varying geometries. However, in closely spaced geometries coupled with large aspect ratios, e.g. a large parallel plate capacitor with very small separation gap, the problem exhibits several challenges. Firstly, the close proximity of the field and source elements presents problems with convergence in numerical evaluations of the interactions between them. Secondly, the aspect ratio of the geometry gives an approximation whereby to far field points, the source contributions from locations that are far apart appear to cancel each other. This leads to high condition numbers in the system matrix. This thesis explores the potential solution to these problems as well as the application of modular fast and direct (MFD) [2] solver to expedite the solution of such problems.

Solar energy is an abundant renewable source, which is expected to play an increasing role in the grid's future infrastructure for distributed generation. The research described in the thesis focuses on the analysis of integrating multi-megawatt photovoltaics (PV) systems with battery energy storage into the existing grid and on the theory supporting the electrical operation of components and systems. The PV system is divided into several sections, each having its own DC-DC converter for maximum power point tracking and a two-level grid connected inverter with different control strategies. The functions of the battery are explored by connecting it to the system in order to prevent possible voltage fluctuations and as a buffer storage in order to eliminate the power mismatch between PV array generation and load demand. Computer models of the system are developed and implemented using the PSCADTM/EMTDCTM software.

Fault location within electric power systems is an important topic that helps reduce outage duration and increases reliability of the system. This paper explores the topic of fault location using traveling waves generated by fault conditions and the discrete wavelet transform used for time-frequency analysis. The single-ended and double-ended traveling wave methods are presented and evaluated on a single circuit and double circuit 500kV system modeled using MATLAB SIMULINK. Results are compared on the basis of wavelet used for analysis, sampling rate, and fault resistance.

Axial flux permanent magnet (AFPM) machines have recently attracted significant attention due to several reasons, such as their specific form factor, potentially higher torque density and lower losses, feasibility of increasing the number of poles, and facilitating innovative machine structures for emerging applications. One such machine design, which has promising, high efficiency particularly at higher speeds, is of the coreless AFPM type and has been studied in the dissertation together with more conventional AFPM topologies that employ a ferromagnetic core. A challenge in designing coreless AFPM machines is estimating the eddy current losses. This work proposes a new hybrid analytical and numerical finite element (FE) based method for calculating ac eddy current losses in windings and demonstrates its applicability for axial flux electric machines. The method takes into account 3D field effects in order to achieve accurate results and yet greatly reduce computational efforts. It is also shown that hybrid methods based on 2D FE models, which require semi-empirical correction factors, may over-estimate the eddy current losses. The new 3D FE-based method is advantageous as it employs minimum simplifications and considers the end turns in the eddy current path, the magnetic flux density variation along the effective length of coils, and the field fringing and leakage, which ultimately increases the accuracy of simulations. After exemplifying the practice and benefits of employing a combined design of experiments and response surface methodology for the comparative design of coreless and conventional AFPM machines with cores, an innovative approach is proposed for integrated design, prototyping, and testing efforts. It is shown that extensive sensitivity analysis can be utilized to systematically study the manufacturing tolerances and identify whether the causes for out of specification performance are detectable. The electromagnetic flux path in AFPM machines is substantially 3D and cannot be satisfactorily analyzed through simplified 2D simulations, requiring laborious 3D models for performance prediction. The use of computationally expensive 3D models becomes even more challenging for optimal design studies, in which case, thousands of candidate design evaluations are required, making the conventional approaches impractical. In this dissertation a new two-level surrogate assisted differential evolution multi-objective optimization algorithm (SAMODE) is developed in order to optimally and accurately design the electric machine with a minimum number of expensive 3D design evaluations. The developed surrogate assisted optimization algorithm is used to comparatively and systematically design several AFPM machines. The studies include exploring the effects of pole count on the machine performance and cost limits, and the systematic comparison of optimally designed single-sided and double-sided AFPM machines. For the case studies, the new optimization algorithm reduced the required number of FEA design evaluations from thousands to less than two hundred. The new methods, developed and presented in the dissertation, maybe directly applicable or extended to a wide class of electrical machines and in particular to those of the PM-excited synchronous type. The benefits of the new eddy current loss calculation and of the optimization method are mostly relevant and significant for electrical machines with a rather complicated magnetic flux path, such is the case of axial flux and of transvers flux topologies, which are a main subject of current research in the field worldwide.

Many systems designed by electrical & computer engineers rely on electromagnetic (EM) signals to transmit, receive, and extract either information or energy. In many cases, these systems are large and complex. Their accurate, cost-effective design requires high-fidelity computer modeling of the underlying EM field/material interaction problem in order to find a design with acceptable system performance. This modeling is accomplished by projecting the governing Maxwell equations onto finite dimensional subspaces, which results in a large matrix equation representation (Zx = b) of the EM problem. In the case of integral equation-based formulations of EM problems, the M-by-N system matrix, Z, is generally dense. For this reason, when treating large problems, it is necessary to use compression methods to store and manipulate Z. One such sparse representation is provided by so-called H^2 matrices. At low-to-moderate frequencies, H^2 matrices provide a controllably accurate data-sparse representation of Z. The scale at which problems in EM are considered ``large'' is continuously being redefined to be larger. This growth of problem scale is not only happening in EM, but respectively across all other sub-fields of computational science as well. The pursuit of increasingly large problems is unwavering in all these sub-fields, and this drive has long outpaced the rate of advancements in processing and storage capabilities in computing. This has caused computational science communities to now face the computational limitations of standard linear algebraic methods that have been relied upon for decades to run quickly and efficiently on modern computing hardware. This common set of algorithms can only produce reliable results quickly and efficiently for small to mid-sized matrices that fit into the memory of the host computer. Therefore, the drive to pursue larger problems has even began to outpace the reasonable capabilities of these common numerical algorithms; the deterministic numerical linear algebra algorithms that have gotten matrix computation this far have proven to be inadequate for many problems of current interest. This has computational science communities focusing on improvements in their mathematical and software approaches in order to push further advancement. Randomized numerical linear algebra (RandNLA) is an emerging area that both academia and industry believe to be strong candidates to assist in overcoming the limitations faced when solving massive and computationally expensive problems. This thesis presents results of recent work that uses a random sampling method (RSM) to implement algebraic operations involving multiple H^2 matrices. Significantly, this work is done in a manner that is non-invasive to an existing H^2 code base for filling and factoring H^2 matrices. The work presented thus expands the existing code's capabilities with minimal impact on existing (and well-tested) applications. In addition to this work with randomized H^2 algebra, improvements in sparse factorization methods for the compressed H^2 data structure will also be presented. The reported developments in filling and factoring H^2 data structures assist in, and allow for, the further pursuit of large and complex problems in computational EM (CEM) within simulation code bases that utilize the H^2 data structure.