<p>Recent developments in semiconductor device technology have seen the advent of wide-bandgap (WBG) based devices that enable operation at high switching frequencies. These devices, such as silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs), are becoming a favored choice in inverters for electric drive systems because of their lower switching losses and higher allowable operating temperature. However, the fast switching of such devices implies increased voltage edge rates (high <i>dv/dt</i>) that give rise to various undesirable effects including large common-mode currents, electromagnetic interference, transient overvoltages, insulation failure due to the overvoltages, and bearing failures due to</p> <p>microarcs. With increased use of these devices in transportation and industrial applications, it is imperative that accurate models and efficient simulation tools, which can predict these high-frequency effects and accompanying system losses, be established. This research initially focuses on establishing an accurate wideband model of a surface-mount permanent-magnet</p> <p>ac machine supplied by a WBG-based inverter. A new multirate simulation framework for predicting the transient behavior and estimating the power losses is then set forth. In this approach,</p> <p>the wideband model is separated into high- and low-frequency models implemented using two different computer programs that are best suited for the respective time scales. Repetitive execution of the high-frequency model yields look-up tables for the switching losses in the semiconductors, electric machine, and interconnecting cable. These look-up tables are then incorporated into the low-frequency model that establishes the conduction</p> <p>losses. This method is applied to a WBG-based electric drive comprised of a SiC inverter and permanent-magnet ac machine. Comparisons of measured and simulated transients are provided.</p>

<p>CO₂ concentration in the atmosphere poses a great threat to life on earth as we know it. The reduction of CO₂ concentration is key to avoid the critical turning point of 1.5^oC temperature increase highlighted by Intergovernmental Panel on Climate Change (IPCC). Gasification using CO₂ as reacting agent can potentially reduce the CO₂ concentration in the atmosphere. Naturally, biomass such as corn, uses great amounts of CO₂ for photosynthesis and produces O₂; hence, energy and fuel production using biomass can potentially be classified as carbon neutral. Moreover, if CO₂ is used as the gasifying agent, gasification can effectively be carbon-negative and collaborate to the reduction of CO2 in the atmosphere.</p> <p>The major setback of using CO₂ biomass gasification is the energy-intensive reaction between C + CO₂ -> 2CO. This reaction at atmospheric pressure and room temperature is heavily tilted towards producing char and CO2. The current investigation describes efforts to favor the right hand side of the reaction by using simple impregnation techniques and cost-effective catalysts to reduce the energy requirements of the reaction. Also, parameters such as pressure are explored to tilt the balance towards the production of CO. Corn stover is selected as the biomass for the present research due to its wide use and availability in the US.</p> <p>The results show that by using catalysts such as iron nitrate and sodium aluminate, the temperature required to achieve substantial char conversion is reduced. Also, increasing the pressure of the reactor, the temperature can be substantially decreased by 100 K and 150 K. The structure and chemical composition of the chars is studied to explain the differences in the reaction rate between chars. Further, chemical kinetics are calculated to compare the present work with previous work in the literature. Finally, data-driven analysis of the gasification data is explored. The appendices provide supplementary information on the application of deep learning to CO₂ recycling using turbulent flames and efforts to reduce the flame spread rate over a pool of Jet A by using Multi Walled Carbon Nanotubes (MWCNTS).</p>

The needs to the sustainable development of electricity, energy efficiency improvement, and environment pollution reduction have favored the development of distributed generation (DG). But the problems come with increasing DG penetration in distribution networks. This thesis presents the Solar Energy Grid Integration System (SEGIS) Stage III project done by Portland General Electric (PGE), Advanced Energy, Sandia National Lab on a PGE selected distribution feeder. The feeder has six monitored commercial solar PV systems connected. The total power output from the PV systems has the potential to reach 30% of the feeder load. The author analyzes the performance of the solar feeder on both generation and voltage effects. As a project report, it introduced a new islanding detection done by other team members to give an islanding solution of future high penetration distribution networks. At last, the author describes micro-grid and grid support concepts in a SEGIS concept paper with some examples.

In power distribution system planning, it is essential to understand the impacts that electric vehicles (EVs), and the non-linear, time-variant loading profiles associated with their charging units, may have on power distribution networks. This research presents a design methodology for the creation of both analytical and behavioral models for EV charging units within a VHDL-AMS simulation environment. Voltage and current data collected from Electric Avenue, located on the Portland State University campus, were used to create harmonic profiles of the EV charging units at the site. From these profiles, generalized models for both single-phase (Level 2) and three-phase (Level 3) EV chargers were created. Further, these models were validated within a larger system context utilizing the IEEE 13-bus distribution test feeder system. Results from the model's validation are presented for various charger and power system configurations. Finally, an online tool that was created for use by distribution system designers is presented. This tool can aid designers in assessing the impacts that EV chargers have on electrical assets, and assist with the appropriate selection of transformers, conductor ampacities, and protection equipment & settings.

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters.

The main focus of this thesis is to test a model that compares the present grid and a Smart Grid system. The thesis discusses the major issues faced by our electricity infrastructure and the possible solutions offered by the Smart Grid. Present grid limitations based on operational, technological, planning, and policy issues are covered. The thesis initially focuses on the limitations of our present grid, and describes severe limitations of our current grid during blackouts. The thesis outlines possible solutions for these problems offered by the concept of the Smart Grid, whose technology and features are described in detail. The thesis details Smart Grid technologies for power generation and the latest electronic devices available to aid the current aging power grid. Further, this thesis offers an analysis that compares the ‘present grid’ to a particular ‘Smart Grid’ configuration consisting of a Combined-Heat & Power (CHP) plant, a Photovoltaic system, and a Demand Response with real-time pricing. The analysis reveals the economic and operational benefit of the Smart Grid system under consideration.

The capacity value (CV) of a power generation unit indicates the extent to which it contributes to the generation system adequacy of a region’s bulk power system. Given the capricious nature of the wind resource, determining wind generation’s CV is nontrivial, but can be understood simply as how well its power output temporally correlates with a region’s electricity load during times of system need. Both wind generation and load are governed by weather phenomena that exhibit variability across all timescales, including low frequency weather cycles that span decades. Thus, a data-driven determination of wind’s CV should involve the use of long-term (i.e., multiple decades) coincident load and wind data. In addition to the challenge of finding high-quality, long-term wind data, existing load data more than several years old is of limited utility due to shifting end usage patterns that alter a region’s electricity load profile. Due to a lack of long-term data, current industry practice does not adequately account for the effects of weather variability in CV calculations. To that end, the objective of this thesis is to develop a model to “hindcast” what the historic regional load in New England would have been if governed by the conjoined influence of historic weather and a more current load profile. Modeling focuses exclusively on summer weekdays since this period is typically the most influential on CV. The summer weekday model is developed using multiple linear regression (MLR), and features a separate hour-based model for eight sub-regions within New England. A total of eighty-four candidate weather predictors are made available to the model, including lagged temperature, humidity, and solar insolation variables. A reanalysis weather dataset produced by the National Aeronautics and Space Administration (NASA) – the Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset – is used since it offers data homogeneity throughout New England over multiple decades, and includes atmospheric fields that may be used for long-term wind resource characterization. Weather regressors are selected using both stepwise regression and a genetic algorithm(GA) based method, and the resulting models and their performance are compared. To avoid a tendency for overfitting, the GA-based method employs triple cross-validation as a fitness function. Results indicate a regional mean absolute percent error (MAPE) of less than 3% over all hours of the summer weekday period, suggesting that the modeling approach developed as part of this research has merit and that further development of the hindcasting model is warranted.

Passively powered radio frequency (RFID) tags are a class of devices powered via harvested ultra high frequency (UHF) radiation emitted by a reader device. Currently, these devices are relegated to little more than a form of wireless barcode, but could be used in a myriad of applications from simple product identification to more complex applications such as environmental sensing. Because these devices are intended for large scale deployment and due to the limited power that can be harvested from RF energy, hardware and cost constraints are extremely tight. The Electronic Product Code (EPC) Global Class 1 Generation 2 (Gen2) specification [EPC08] is currently the de facto communication standard for passively powered RFID. One issue restricting deployment and a cause for some privacy concerns is a lack of security in the Gen2 protocol. We will demonstrate a potential solution to this problem by using a novel block cipher designed for low power and area constrained devices to encrypt and transmit sensor data. This will be done while maintaining backward compatibility with the original standard and will require no substantial changes to the reader. Our solution will also provide one way authentication, data integrity checking and will provide security against replay attacks. In this thesis we will demonstrate an FPGA emulation of a Gen2 compatible RFID tag which will serve as a test bed for several novel features. We will leverage prior work involving several aspects of a tag [QL09] [PP07] as well as incorporate a novel low power encryption cipher [AB07] and external temperature sensor. Demonstrated in [CT08], FPGA emulation will allow for the independent verification of several components. This thesis will provide insight into the future of RFID and will provide insight into tag design as well as possible future updates to the Gen2 standard.

Energy consumption and speed of execution have long been recognized as conflicting requirements for processor design. In this work, we have developed a low-cost dynamic architecture adaptation scheme to save leakage power in caches. This design uses voltage scaling to implement drowsy caches. The importance of a dynamic scheme for managing drowsy caches, arises from the fact that not only does cache behavior change from one application to the next, but also during different phases of execution within the same application. We discuss various implementations of our scheme that provide a tradeoff between granularity of control and design complexity. We investigate a combination of policies where the cache lines can be turned off completely if they are not accessed, when in the drowsy mode. We also develop a simple dynamic cache-way shutdown mechanism, and propose a combination of our dynamic scheme for drowsy lines, with the cache-way shutdown scheme. Switching off cache ways has the potential of greater energy benefits but provides a very coarse grained control. Combining this with the fine grained scheme of drowsy cache lines allows us to exploit more possibilities for energy benefits without incurring a significant degradation in performance. Keywords: Drowsy Cache, Architecture Adaptation, Low Power, Leakage Reduction, Dynamic Scheme

Quantum-Dot Cellular Automata (QCA) is currently being investigated as an alternative to CMOS technology. There has been extensive study on a wide range of circuits from simple logical circuits such as adders to complex circuits such as 4-bit processors. At the same time, little if any work has been done in considering the possibility of reconfiguration to reduce power in QCA devices. This work presents one of the first such efforts when considering reconfigurable QCA architectures which are expected to be both robust and power efficient. We present a new reconfiguration scheme which is highly robust and is expected to dissipate less power with respect to conventional designs. An adder design based on the reconfiguration scheme will be presented in this thesis, with a detailed power analysis and comparison with existing designs. In order to overcome the problems of routing which comes with reconfigurability, a new wire crossing mechanism is also presented as part of this thesis.