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It is estimated that close to 90% of outages in the electric power grid originate in the distribution system. Although the use of advanced metering infrastructure has increased situational awareness in the distribution system, current approaches to outage management are often reactive and do not fully leverage insights from outage prediction models for the service restoration process. Hence, this work aims to provide a holistic strategy for combining outage prediction and service restoration in the outage management process. First, a detailed analysis of an outage dataset is conducted in order to gain insights into the frequency and duration of outages in a distribution system. Two machine learning techniques, random forest and gradient boosting, are used to rank outage features, including outage causes, climate descriptions, and failed equipment, to determine which outage features have the greatest impact on average outage duration in a distribution system. Following that, this dissertation proposes a proactive topology optimization and service restoration framework that leverages forecasts from outage prediction models to mitigate the impacts of predicted outages in the distribution system. The proposed framework is formulated as a mixed integer linear programming (MILP) problem with the objectives of minimizing the load lost prior to the outage and maximizing the restorable load when the outage occurs at the predicted locations. The MILP model was simulated using the Python Optimization Modeling Objects (Pyomo) package, an open-source tool, and solved using the CPLEX solver. Using modified versions of the IEEE 13-node and 123-node test feeders, the framework considers three optimization cases: single outage, multiple outage, and weighted multiple outage. In addition, a sensitivity analysis based on the weighted multiple outage case is presented in order to determine the optimal topology to operate in given a range of probabilities for the possible outage locations in the distribution system. Furthermore, the MILP model used in this work is validated by comparing its power flow results with those obtained from OpenDSS, an open-source simulation tool for electric power distribution systems. The results show that the MILP model provides a reasonable approximation of the nonlinear power flow model. Overall, this dissertation provides a method for improving situational awareness within the distribution system. Using the proposed approach, distribution system operators can determine what topology to operate in ahead of predicted outages, thereby reducing the loads left out of service.
Students struggle with mental health needs. Student support staff are, within the school, regularly the first employees to encounter and respond to the students’ specific mental health needs. It is not uncommon for students’ mental health needs to be professionally undiagnosed and professionally unsupported. Barriers to professional treatment are plentiful and when students lack support for their mental health needs, academic and social issues are exacerbated. For these and other reasons, schools struggle to respond to students’ mental health needs. The purpose of this qualitative study was to understand the lived experiences of student support staff (school counselors, school social workers, and school nurses) who work in small magnet high schools as they work to support students with mental health needs. In-depth, semi-structured interviews were conducted with seven student support staff from three different small magnet high schools within a public school district in North Carolina. After coding the transcribed interviews, four themes emerged from the data: 1) Magnet school student support staff are not professional therapists. 2) Magnet school student support staff struggle to perform their duties/roles. 3) Academic rigors and expectations in magnet high schools contribute to students' mental health needs. 4) Barriers to students abound and some are unique to the magnet school experience. Utilization of existing support structures such as Individualized Education Plans (IEPs) and/or 504 Plans and the Multi-tiered Systems of Support (MTSS) framework is recommended in conjunction with research-based mental health support programs to assist student support staff and schools in supporting students’ mental health needs.
Firms must continually adapt themselves to survive and outperform competition during rapidly changing environments, both predictable and unpredictable. The dynamic capabilities theory provides a framework for firms to renew and adapt their organizational competencies and capabilities to achieve congruence with the changing market environment. Research has shown that this leads to sustained competitive advantage and superior firm performance. However, resilience during a disruptive event requires the firms to intentionally deviate from their patterned capability-building strategies to respond differentially to turn a challenging environment into a growth opportunity. There is a gap in the literature to empirically examine this resilience phenomenon and the extent to which dynamic capabilities gained during typical business environments assist in achieving resilience during disruption. In addition, the role of digital maturity in leveraging technology for transforming the business and social capital gained through extracting value from business ties needs to be better understood. This study empirically investigates these gaps by integrating dynamic capabilities and organizational resilience theories. The data was collected through a quantitative survey from senior executives of established, mid-to-large firms within the United States, and the results provide theoretical and practical contributions.
The goal of this qualitative research study was to understand middle school principals’ perceptions of leading a transition from traditional grading to standards-based grading (SBG). Using a multiple case study approach six middle school principals who work in a school district that had successfully implemented SBG in elementary school, but not in middle schools, participated in the study. This study took place during the COVID-19 pandemic, thus the study also sought to understand how principals perceived the impacts from COVID-19 on their leadership of SBG. A demographic survey was sent to each participant that collected demographic data, and semi-structured interviews were conducted with each principal. Using the constant comparison method, four themes emerged: (a) misalignment between PLC practices and individual teachers’ grading practices exists, (b) a lack of district level SBG report card grading progressions from elementary school to middle school has created confusion and frustration, (c) parents and teachers expect traditional grading in middle school to prepare adolescents for high school, and (d) leading SBG was not a priority following the COVID-19 pandemic. The findings of this study also used Bandura’s Social Cognitive Theory (SCT) as a framework to understand how principals’ self-efficacy impacts that of their teachers and how collective efficacy can be improved by increasing the principals’ self-efficacy as an instructional leader. Implications of this study include suggestions for principals to increase their knowledge and self-efficacy through professional development, collaboration, and district level supports. The unique role of middle school culture, including grading practices is discussed.
Exploring the Effects of a Daily Audio-Guided Mindfulness Intervention for Teachers.
(Under the direction of DR. REBBECA SHORE, COMMITTEE CHAIR)
Teachers are experiencing high levels of stress resulting in higher attrition rates that are impacting student achievement and placing considerable burdens on schools. The practice of mindfulness in the classroom may be a way to help teachers cope with perceived stress, be aware of mindfulness, and increase self-efficacy. The purpose of this quantitative study was to investigate the effects of teacher participation in a daily audio-guided mindfulness intervention to determine if the practice of mindfulness for nine weeks affects their perceived stress levels, awareness of mindfulness, and self-efficacy. The researcher used pre-survey and post-survey data from the Mindfulness Awareness Attention Scales (MAAS), the Perceived Stress Scales (PSS), and the Teacher Sense of Efficacy Scales Survey (TSES) Short Form and program efficacy data was collected during the intervention. The study consisted of 41 participants.
In this study, the researcher found that there was little to no significant change in teachers’ perceived stress levels and awareness of mindfulness. There was a small increase in teachers’ perceived self-efficacy. The research within this study describes mindfulness, the benefits to individuals who practice mindfulness, and the benefits that can be evident in the educational setting with teachers and students. It is evident that there is a place for mindfulness within the schoolhouse, but the path to mindfulness may require consistency over long periods of time, and time is not always a luxury schools can spare. For the program to succeed and see lasting impacts on teachers’ perceived stress, awareness of mindfulness, and increase in self-efficacy, it may take consistency over time, and teachers’ acceptance of mindfulness.
Institutional betrayal occurs when an organization perpetrates wrongdoing against an individual who depends on that system. There are serious known consequences of healthcare institutional betrayal including patient disengagement from care and healthcare organization distrust (Smith, 2017). The aim of this study was to examine specific reparative actions following institutional betrayal to determine the effect of repair behaviors on institutional betrayal, trust, expectations for future healthcare encounters, and intentions to avoid or disengage from healthcare. Undergraduate participants (N=198; 58% women; 53% White) read a vignette depicting institutional betrayal in a healthcare setting. They then completed measures of institutional betrayal, trust in healthcare, healthcare avoidance/disengagement, and expectations for future healthcare encounters. Next, participants were randomly assigned to one of four repair vignettes and completed measures a second time. As predicted, participants who were randomly assigned to one of the repair conditions (vs. control) reported significantly lower institutional betrayal scores at post-test, higher positive expectations for future healthcare encounters, and higher levels of trust. Overall, results from this study indicate that reparative actions following healthcare institutional betrayal influenced participants’ self-reported beliefs about the healthcare system. Given the documented negative sequelae to healthcare institutional betrayal, this study’s finding that relatively small actions can facilitate individual-system repair is clinically meaningful.
Within established organizations, employee innovative behavior is vital for the long-term survival of firms. Employee individual entrepreneurial orientation (IEO) represents tendencies by an individual toward innovativeness, proactiveness, and risk-taking behaviors. IEO is an emerging construct in the entrepreneurship domain, and while an important construct, contextual factors can influence the employee’s demonstration of innovative work behavior (IWB). The degree of IEO similarity or fit between an employee and their supervisor is theorized to amplify the employee’s innovative work behavior. To guide this research, person-supervisor fit (P-S fit) theory and strategic consensus theory provided a theoretical framework to investigate the relationships between these constructs. Drawing on a sample of employees across all levels of an established firm, two sets of data were analyzed: perceptions of supervisor IEO fit (n = 265) and matched pairs of IEO between employee-supervisor dyads (n = 132). Results suggest that employees’ levels of innovativeness and proactiveness are positively associated with IWB. Additionally, the data suggest that the level of proactiveness and risk-taking fit have mixed findings, suggesting that the supervisor IEO fit magnifies the IEO-IWB relationship when the employee has low levels of proactiveness and risk-taking tendencies. This research has theoretical and practical implications by examining individual level EO, within the context of supervisor fit, in promoting innovative behavior in the workplace.
The digital era has brought revolutionary automation technology in various science and engineering domains.
Integration of large-scale sustainable Distributed Energy Resources (DERs) in the power distribution network has led to the decentralization of power generation, contrary to the conventional power grid. Supplementary information and communication technology, decentralized digital economic structures, and data-driven learning-based technology have transformed distribution networks as a system of systems in recent decades. Thus the ecosystem surrounding the electricity consumer is getting socially, economically, and politically complex. There is a strong requirement for a technology framework that can offer operational and managerial independence to the geographically distributed consumer base. In the context of power distribution network operation, the need for decentralized hierarchical control structures is dictated by the rapid integration of sustainable DERs. To tackle the contemporary problems of climate change, 100\% sustainable generation-based power grid operations can be a milestone toward carbon neutral society. The nature of such power networks entails a variety of properties including variability in a generation, bidirectional power flow, and nonlinear network dynamics due to complex generation and load mix. The key operational challenge is the coexistence of large-scale DERs to achieve stable and accurate load power sharing while regulating the voltage and frequency in the network to the nominal values. The technology solution needs to be scalable by reducing the dependency on the communication network, robust against the measurement noise, and adaptive to the changing network dynamics. Furthermore, to enhance the overall resiliency of the operation, network dynamics have to be systematically studied and optimal network reconfiguration methodology has to be devised. The vision of the dissertation work is to formulate a hierarchical decentralized control structure to accommodate three-level research objectives. Firstly, at the DER level, considering the low \textit{X/R} and unbalanced nature of the distribution network, appropriate cascaded primary control loops are designed. A unified control architecture is proposed for stable multiple DER power sharing, achieving ride-through capability, and maintaining the network voltage and frequency close to nominal values. The unified control architecture is devised through a systematic definition of steady-state operating modes and the interaction among hierarchical entities in the grid. Secondly, at the microgrid level, a decentralized predictive optimal constrained secondary control framework to maintain the nominal voltage and frequency is formulated. The proposed strategy is built on a first-order model of the primary controller and local/global measurements-based state estimation, facilitating the deployability to grid edge devices. The framework is further extended to incorporate a data-driven approach when model parameters are not available. Lastly, at the network level, detailed network dynamics are modeled as a real-time environment by incorporating primary, and secondary control and protection functions. The reinforcement learning agent is designed by utilizing an extended q-routing methodology, which interacts with the environment through event-driven communication and performs optimal network reconfiguration during events in the environment. The ultimate purpose of this dissertation work is to bring value to engaged stakeholders in the process of achieving 100% sustainable power grid. There exists a gap between the aforementioned hierarchical technology solutions and business delivery models. This gap is addressed in chapter 7 by fostering a market for resiliency services through the Energy-as-a-Service model. The regulatory framework and ownership agreements are yet to evolve to support the delivery model acceptable to the involved stakeholders. Sophisticated technology aggregation and cost structure must be achieved through systematic economic analysis to maximize the revenue for technology and service providers. The author, lastly and most importantly, emphasizes the need for sustainable business model innovation in coordination with the big technology players, new players such as startups including aggregators, and utilities to position themselves in the market.
In recent years, Wide Bandgap (WBG) semiconductor based power devices has matured rapidly and are playing a significant role in high switching frequency power electronic applications. WBG materials such as silicon carbide (SiC) and gallium nitride (GaN) possess a higher critical breakdown strength, an increased thermal conductivity, and a wider energy bandgap than silicon which make WBG semiconductors as a material of choice in low on-resistance, high blocking voltage, high switching frequency and high operating temperature power applications. In addition, using these devices result in the overall size reduction of the devices as higher doping levels can be achieved at similar voltage levels.
A gate driver acts as an interface between power devices and logic-level control signals and plays a significant role in the switching behavior of WBG devices. To increase the overall efficiency and reduce the footprint of the system high switching frequency operation of the devices is desirable. However, power consumption in the gate driving circuit increases with frequency. A viable strategy to reduce the gate driving power consumption is to use resonant gate driving technique where part of the energy stored in the gate capacitance is recycled.
In this dissertation, a novel resonant gate driver (RGD) for WBG devices is proposed which drives the semiconductor device using quasi-square wave by utilizing higher order harmonics. Firstly, the operating principles of the proposed gate driver circuit is presented. Secondly, a detailed characteristic analysis and power loss analysis of the circuit are provided. Additionally, a comprehensive simulation study of the proposed circuit is introduced. Moreover, a prototype of the proposed RGD was built and tested. Experimental results demonstrate that the proposed gate driving technique can significantly reduce power consumption in the gate driver circuit in comparison to conventional gate driving techniques.
One dimensional (1D) nanostructured materials are the materials having one external dimension outside the nanoscale range and the other two dimensions in the nanoscale range such as nanowires (NWs). Due to their unique one dimensionality, 1D NWs are considered as the ideal systems to investigate many foundational concepts in physical science and engineering. Along the same line, NWs can be used to establish correlation between their structural parameters, dimensionality, size, and functional properties. Inspired by the vapor-liquid-solid (VLS) growth mechanism provided by R.S. Wagner, NW synthesis started in the early 1990s. In the following three decades, NW research field expanded significantly in terms of synthesis, characterization of structures and properties. However, correlation of the properties with well-understood NW structures and rational synthesis of different NWs with desired properties are two main roadblocks for the wide application of different types of NWs, which are the main focus of this dissertation.
This dissertation consists of two parts. The first part is a collaborative work with researchers from Vanderbilt University and Pennsylvania State University and the goal is to find if any correlation exists between structural parameters, morphology (such as lattice constants and dimensions) and thermal properties of niobium selenide (NbSe3) NWs. NbSe3 is a chain-like structure with molecular chains joined by van der Waals (VdW) force and suitable for the exploration of the effect of electron-phonon (e-ph) interaction on the thermal conductivity. However, superdiffusive thermal transport was observed for ultra-thin NbSe3 NWs (hydraulic diameter <26 nm), which led us to measure the structural parameters and find interrelation with the observed properties. Individual NW was examined using transmission electron microscope (TEM) to obtain high resolution TEM image, diffraction pattern (DP) and morphology at lower magnifications. But measuring the lattice constants from NbSe3 NW, which is a monoclinic structure, was extremely challenging. It was discovered that the NW needs to be tilted to out-of-plane to obtain all the lattice parameters. The process is constrained due to the tilt limitation in the TEM. A DP roadmap was built from the measured and simulated DPs to facilitate the TEM examination and analysis. The obtained results eliminated the structural effect from the unusual thermal properties, and led to consider other factors (change in heat capacity and Debye temperature) to explain the phenomenon.
For the second part of this work, growth mechanism of boron carbide NWs was explored using TEM-based cross-sectional examination. Boron carbide materials are known for their fascinating chemical and physical properties which arise due to their unusual structural complexity and exceptional bonding. Bulk boron carbides are widely used for refractory, ballistic armor, and nuclear applications. They also have potential applications in the high temperature electronic and thermoelectric devices. In order to obtain improved thermoelectric performance, boron carbide NWs were synthesized and extensive characterization of structures and thermal properties were performed by our previous group members. However, rational synthesis of boron carbide NWs with desired properties could not be obtained yet. For this purpose, a through understanding of growth mechanism is crucial.
In order to explore growth mechanism, cross-sectional TEM examinations were conducted on multiple samples prepared in different experimental conditions. The approach was to identify the effect of different reaction parameters on the NW growth. Cross-sectional sample preparation required strenuous effort and the results largely depended on the quality of the prepared samples. Reaction parameters that were considered include Ni film as catalyst, diborane and methane as precursor gases, and annealing temperature and time. During annealing of Ni film on the SiO2/Si substrate, catalyst-substrate interaction could be observed. The contrast in the TEM images demonstrated that Ni film agglomerated into particles and diffused into the SiO2 layer. Also, void formation in the SiO2 was found which could be nucleated due to the coincidence of microchannels in the SiO2 and stress in the Ni film/substrate interface during annealing. Higher temperature showed a contradictory trend in the agglomeration and void nucleation behavior. In the other two experimental conditions, diborane and methane were added to the reaction chamber separately. Diborane was seen to etch the SiO2 layer and facilitate the particles diffusion whereas methane exhibited the opposite effect. Some nanostructures with a catalyst on top grew in the later case. With diborane and methane together generated thin films with nanostructures in the absence of Ni film. The condition with all reaction parameters generated a thin film on the SiO2 layer. NWs could be growing from this film. However, the NW/thin film/SiO2 interfaces are not clear due to the overlapping layers.
