Master of Science / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / Anil Pahwa / Electric vehicles (EVs) are becoming increasingly popular because of their low operating costs and environmentally friendly operation. However, the anticipated increase of EV usage and increased use of renewable energy sources and smart storage devices for EV charging presents opportunities as well as challenges. Time-varying electricity pricing and day-ahead power commitment adds another dimension to this problem. This thesis, describes development of coordinated EV charging strategies for renewable energy-powered charging stations at homes and parking lots. We develop an optimal control theory-based charging strategy that minimizes power drawn from the electricity grid while utilizing maximum energy from renewable energy sources. Specifically, we derive a centralized iterative control approach in which charging rates of EVs are optimized one at a time. We also propose an algorithm that maximizes profits for parking lot operators by advantageously utilizing time-varying electricity pricing while satisfying system constraints. We propose a linear programming-based strategy for EV charging, and we specifically derive a centralized linear program that minimizes charging costs for parking lot operators while satisfying customer demand in available time. Then we model EV charging behavior of Active Consumers. We develop a real-time pricing scheme that results in favorable load profile for electric utility by influencing EV charging behavior of Active Consumers. We develop this pricing scheme as a game between electric utility and Active Consumers, in which the electric utilities decide optimal electricity prices that minimize peak-to-average load ratio and Active Consumers decide optimal charging strategy that minimizes EV charging costs for Active Consumers.

Master of Science / Electrical and Computer Engineering / William B. Kuhn / At RF frequencies and beyond, metallic circuit interconnects no longer behave as lumped-element wires; instead they exhibit distributed-element behavior and are classified as transmission lines. Power losses on transmission lines are of great concern to RF and microwave engineers and great care is taken to minimize power losses while still maintaining application-based robustness. The combination of low-temperature co-fire ceramics (LTCCs) and thin-film transmission line fabrication allows application-specific robustness and excellent microwave and millimeter wave performance to be achieved. LTCC technology provides a low-loss microwave substrate and allows for thin-film metal and insulator depositions to form precision transmission-line geometries and surface-applique capacitors. In the field of thin-film metals however, concern over excess power losses at high frequencies has arisen due to the necessity of a high-resistance metallic adhesion layer which is required for the mechanical adhesion of the transmission lines to the LTCC substrate. This is especially worrisome in a microstrip configuration where the current density is concentrated at the substrate-metal interface; exactly where the high-loss metal is situated. This thesis shows that if the high-resistance adhesion layer is limited to a thickness which is a fraction of its skin depth, with more conductive metals layered above, then those excessive resistive losses can be avoided. Issues with decreasing the total thickness of the thin-film layered metals are also investigated to achieve better interconnect line-and-space resolution, which is required for electronics operating at millimeter-wave bandwidths. Several test cases show that thinning of the metal layers has minimal impact on electrical performance. However, poor signal integrity is observed when the finished thickness of the metal stack up is reduced below 1μm. Further testing reveals that surface roughness leads to manufacturing issues when trying to produce thin-films with thicknesses in the sub-micron range. Finally, a novel bypass and coupling capacitor topology is proposed and investigated. The capacitors are simple thin-film metal-insulator-metal constructions designed for use in a flip-chip mounting environment. Testing shows the capacitors exhibit a very low impedance through 20 GHz making them an ideal board-level bypass solution. This technology has the potential to replace all but the large bulk charge storage capacitors in electronic designs, increasing performance and mechanical robustness, while simultaneously decreasing bill of material cost and PCB assembly times.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Don Gruenbacher / Warren White / This dissertation presents nonlinear control schemes to improve the productivity and lifespan of doubly fed induction generator (DFIG)-based and permanent magnet generator (PMG)-based variable speed wind turbines. To improve the productivity, a nonlinear adaptive control scheme is developed to maximize power capture. This controller consists of three feedback loops. The first loop controls electrical torque of the generator in order to cancel the nonlinear term of the turbine equation of motion using the feedback linearization concept. The nonlinearity cancelation requires a real-time estimation of aerodynamic torque. This is achieved through a second loop which estimates the ratio of the wind turbine power capture versus the available wind power. A third loop utilizes this estimate to identify the shaft speed at which the wind turbine operates at a greater power output. Contrary to existing techniques in literature, this innovative technique does not require any prior knowledge of the optimum tip speed ratio. The presented technique does not need a dither or perturbation signal to track the optimum shaft speed at the maximum power capture. These features make this technique superior to existing methods. Furthermore, the lifespan of variable speed wind turbines is improved by reducing stress on the wind turbine drivetrain. This is achieved via developing a novel vibration mitigation technique using sliding-mode control theory. The technique measures only generator speed as the input signal and then passes it through a high-pass filter in order to extract the speed variations. The filtered signal and its integral are then passed through identical band-pass filters centered at the dominant natural frequency of the drivetrain. These two signals formulate a sliding surface and consequently a control law to damp the drivetrain torsional stress oscillations caused by electrical and mechanical disturbances. This technique provides a robust mitigation approach compared with existing techniques. These control schemes are verified through holistic models of DFIG- and PMG-based wind turbines. Except for wind turbine aerodynamics, for which an existing simulator is used, the developed models of all components including DFIG, PMG, converters, multi-mass drivetrain, and power line are presented in this dissertation.

Doctor of Philosophy / Electrical and Computer Engineering / Ruth D. Miller / Optimizing renewable distributed generation in distribution systems has gained popularity with changes in federal energy policies. Various studies have been reported in this regard and most of the studies are based on optimum wind and/or solar generation planning in distribution system using various optimization techniques such as analytical, numerical, and heuristic. However, characteristics such as high energy density, relatively lower footprint of land, availability, and local reactive power compensation ability, have gained increased popularity for optimizing distributed wind generation (DWG) in distribution systems. This research investigated optimum distributed generation planning (ODGP) using two primary optimization techniques: analytical and heuristic. In first part of the research, an analytical optimization method called “Combined Electrical Topology (CET)” was proposed in order to minimize the impact of intentional structural changes in distribution system topology, in distributed generation/ DWG placement. Even though it is still rare, DWG could be maximized to supply base power demand of three-phase unbalanced radial distribution system, combined with distributed battery energy storage systems (BESS). In second part of this research the usage of DWG/BESS as base power generation, and to extend the ability to sustain the system in a power grid failure for a maximum of 1.5 hours was studied. IEEE 37-node, three-phase unbalanced radial distribution system was used as the test system to optimize wind turbines and sodium sulfide (NaS) battery units with respect to network real power losses, system voltage profile, DWG/BESS availability and present value of cost savings. In addition, DWG’s ability to supply local reactive power in distribution system was also investigated. Model results suggested that DWG/NaS could supply base power demand of a threephase unbalanced radial distribution system. In addition, DWG/NaS were able to sustain power demand of a three-phase unbalanced distribution system for 1.5 hours in the event of a power grid failure.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / In recent years, there has been a rapid expansion in the development and use of low-power, low-cost wireless modules with sensing, computing, and communication functionality. A wireless sensor network (WSN) is a group of these devices networked together wirelessly. Wireless sensor networks have found widespread application in infrastructure, environmental, and human health monitoring, surveillance, and disaster management. While there are many interesting problems within the WSN framework, we address the challenge of energy availability in a WSN tasked with a cooperative objective. We develop approximation algorithms and execute an analysis of concave utility maximization in resource constrained systems. Our analysis motivates a unique algorithm which we apply to resource management in WSNs. We also investigate energy harvesting as a way of improving system lifetime. We then analyze the effect of using these limited and stochastically available communication resources on the convergence of decentralized optimization techniques. The main contributions of this research are: (1) new optimization formulations which explicitly consider the energy states of a WSN executing a cooperative task; (2) several analytical insights regarding the distributed optimization of resource constrained systems; (3) a varied set of algorithmic solutions, some novel to this work and others based on extensions of existing techniques; and (4) an analysis of the effect of using stochastic resources (e.g., energy harvesting) on the performance of decentralized optimization methods. Throughout this work, we apply our developments to distribution estimation and rate maximization. The simulation results obtained help to provide verification of algorithm performance. This research provides valuable intuition concerning the trade-offs between energy-conservation and system performance in WSNs.

Master of Science / Department of Electrical and Computer Engineering / Anil Pahwa / The unusual weather events during the polar vortex of 2014 illuminated the needs for fuel diversity for power generation in order to allow reliable operation of the electricity grid. A system wide reliability assessment for winter months should be undertaken in addition to the summer months to ensure reliable operation of the electricity grid throughout the year. Severe weather conditions that lead to equipment malfunction during the polar vortex should be thoroughly investigated and remediations to ensure satisfactory future performance of the grid must be undertaken. Environmentally unfriendly emissions from power plants must be minimized but diversity of generation fuel must be maintained. Future energy policies must be formulated with consideration that approximately 14 GW of coal generation in Pennsylvania Jersey Maryland Regional Transmission Organization’s control area available during the polar vortex will be retired by 2015 and replaced with plants that utilize fuel types other than coal.

Master of Science / Department of Electrical and Computer Engineering / Anil Pahwa / Energy costs are rising, supplies of fossil fuels are diminishing, and environmental concerns surrounding power generation in the United States are at an all-time high. The United States is continuing to push all states for energy reform and where better for Kansas to look than wind energy? Kansas is second among all states in wind generation potential; however, the best wind generation sites are located predominantly in sparsely populated areas, creating energy transportation problems. Due to these issues interest in community wind projects has been increasing. To determine the economic potential of community wind generation a distribution system in rural western Kansas where interest in community wind exists was examined and a feasibility study based on historical data, economic factors, and current grid constraints was performed. Since the majority of the load in this area is from pivot-point irrigation systems, load distributions were created based on temperature ranges instead of a linear progression of concurrent days. To test the economic viability three rate structures were examined: flat energy rate, demand rate, and critical peak pricing. A Monte Carlo simulation was designed and run to simulate twenty-year periods based on the available historical data; twenty-year net present worth calculations were performed to ensure economic viability. A sensitivity analysis was then performed to examine the effects of change in turbine size and energy rate scale. Finally, an energy storage analysis was performed to examine the economic viability of various sizes of battery storage systems.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Caterina Scoglio / Critical infrastructures are repeatedly attacked by external triggers causing tremendous amount of damages. Any infrastructure can be studied using the powerful theory of complex networks. A complex network is composed of extremely large number of different elements that exchange commodities providing significant services. The main functions of complex networks can be damaged by different types of attacks and failures that degrade the network performance. These attacks and failures are considered as disturbing dynamics, such as the spread of viruses in computer networks, the spread of epidemics in social networks, and the cascading failures in power grids. Depending on the network structure and the attack strength, every network differently suffers damages and performance degradation. Hence, quantifying the robustness of complex networks becomes an essential task. In this dissertation, new metrics are introduced to measure the robustness of technological and social networks with respect to the spread of epidemics, and the robustness of power grids with respect to cascading failures. First, we introduce a new metric called the Viral Conductance ($VC_{SIS}$) to assess the robustness of networks with respect to the spread of epidemics that are modeled through the susceptible/infected/susceptible ($SIS$) epidemic approach. In contrast to assessing the robustness of networks based on a classical metric, the epidemic threshold, the new metric integrates the fraction of infected nodes at steady state for all possible effective infection strengths. Through examples, $VC_{SIS}$ provides more insights about the robustness of networks than the epidemic threshold. In addition, both the paradoxical robustness of Barab\'si-Albert preferential attachment networks and the effect of the topology on the steady state infection are studied, to show the importance of quantifying the robustness of networks. Second, a new metric $VC_$ is introduced to assess the robustness of networks with respect to the spread of susceptible/infected/recovered ($SIR$) epidemics. To compute $VC_$, we propose a novel individual-based approach to model the spread of $SIR$ epidemics in networks, which captures the infection size for a given effective infection rate. Thus, $VC_$ quantitatively integrates the infection strength with the corresponding infection size. To optimize the $VC_$ metric, a new mitigation strategy is proposed, based on a temporary reduction of contacts in social networks. The social contact network is modeled as a weighted graph that describes the frequency of contacts among the individuals. Thus, we consider the spread of an epidemic as a dynamical system, and the total number of infection cases as the state of the system, while the weight reduction in the social network is the controller variable leading to slow/reduce the spread of epidemics. Using optimal control theory, the obtained solution represents an optimal adaptive weighted network defined over a finite time interval. Moreover, given the high complexity of the optimization problem, we propose two heuristics to find the near optimal solutions by reducing the contacts among the individuals in a decentralized way. Finally, the cascading failures that can take place in power grids and have recently caused several blackouts are studied. We propose a new metric to assess the robustness of the power grid with respect to the cascading failures. The power grid topology is modeled as a network, which consists of nodes and links representing power substations and transmission lines, respectively. We also propose an optimal islanding strategy to protect the power grid when a cascading failure event takes place in the grid. The robustness metrics are numerically evaluated using real and synthetic networks to quantify their robustness with respect to disturbing dynamics. We show that the proposed metrics outperform the classical metrics in quantifying the robustness of networks and the efficiency of the mitigation strategies. In summary, our work advances the network science field in assessing the robustness of complex networks with respect to various disturbing dynamics.

Master of Science / Department of Electrical Engineering / Shelli K. Starrett / Modern power systems are integrated, complex, dynamic systems. Due to the complexity, power system operation and control need to be analyzed using numerical simulation. The load model is one of the least known models among the many components in the power system operation. The two different load models are the static and dynamic models. The ZIP load model has been extensively studied. This has widely applied to composite load models that could maintain constant impedance, constant current, and/or constant power. In this work, various Neural Networks algorithms and fuzzy logic have been used to obtain these ZIP load model coefficients for determining the percentage of constant impedance, current, or power for the various load buses. The inputs are a combination of voltage, voltage change, and power change, or voltage and power, and the outputs consist of the ZIP load model coefficients for determining the type and the percentage of load at the bus. The trained model is used to predict the type and percentage of constant load at other buses using simulated transient data from the 16-generator system. A small study was also done using a dynamic induction machine model in addition to the ZIP load model. As expected, the results show that the dynamic model is more difficult to determine than the static model.

Doctor of Philosophy / Electrical and Computer Engineering / Balasubramaniam Natarajan / A cyber-physical system (CPS) typically consists of networked computational elements that control physical processes. As an integral part of CPS, the widespread deployment of communicable sensors makes the task of monitoring and control quite challenging especially from the viewpoint of scalability and complexity. This research investigates two unique aspects of overcoming such barriers, making a CPS more robust against data explosion and network vulnerabilities. First, the correlated characteristics of high-resolution sensor data are exploited to significantly reduce the fused data volume. Specifically, spatial, temporal and spatiotemporal compressed sensing approaches are applied to sample the measurements in compressed form. Such aggregation can directly be used in centralized static state estimation even for a nonlinear system. This approach results in a remarkable reduction in communication overhead as well as memory/storage requirement. Secondly, an agent based architecture is proposed, where the communicable sensors (identified as agents) also perform local information processing. Based on the local and underdetermined observation space, each agent can monitor only a specific subset of global CPS states, necessitating neighborhood information exchange. In this framework, we propose an agent based static state estimation encompassing local consensus and least square solution. Necessary bounds for the consensus weights are obtained through the maximum eigenvalue based convergence analysis and are verified for a radial power distribution network. The agent based formulation is also applied for a linear dynamical system and the consensus approach is found to exhibit better and more robust performance compared to a diffusion filter. The agent based Kalman consensus filter (AKCF) is further investigated, when the agents can choose between measurements and/or consensus, allowing the economic allocation of sensing and communication tasks as well as the temporary omission of faulty agents. The filter stability is guaranteed by deriving necessary consensus bounds through Lyapunov stability analysis. The states dynamically estimated from AKCF can be used for state-feedback control in a model predictive fashion. The effect of lossy communication is investigated and critical bounds on the link failure rate and the degree of consensus that ensure stability of the agent based control are derived and verified via simulations.