Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Genre: Doctoral Dissertation
- Creators: Si, Jennie
Description
Due to restructuring and open access to the transmission system, modern electric power systems are being operated closer to their operational limits. Additionally, the secure operational limits of modern power systems have become increasingly difficult to evaluate as the scale of the network and the number of transactions between utilities increase. To account for these challenges associated with the rapid expansion of electric power systems, dynamic equivalents have been widely applied for the purpose of reducing the computational effort of simulation-based transient security assessment. Dynamic equivalents are commonly developed using a coherency-based approach in which a retained area and an external area are first demarcated. Then the coherent generators in the external area are aggregated and replaced by equivalenced models, followed by network reduction and load aggregation. In this process, an improperly defined retained area can result in detrimental impacts on the effectiveness of the equivalents in preserving the dynamic characteristics of the original unreduced system. In this dissertation, a comprehensive approach has been proposed to determine an appropriate retained area boundary by including the critical generators in the external area that are tightly coupled with the initial retained area. Further-more, a systematic approach has also been investigated to efficiently predict the variation in generator slow coherency behavior when the system operating condition is subject to change. Based on this determination, the critical generators in the external area that are tightly coherent with the generators in the initial retained area are retained, resulting in a new retained area boundary. Finally, a novel hybrid dynamic equivalent, consisting of both a coherency-based equivalent and an artificial neural network (ANN)-based equivalent, has been proposed and analyzed. The ANN-based equivalent complements the coherency-based equivalent at all the retained area boundary buses, and it is designed to compensate for the discrepancy between the full system and the conventional coherency-based equivalent. The approaches developed have been validated on a large portion of the Western Electricity Coordinating Council (WECC) system and on a test case including a significant portion of the eastern interconnection.
ContributorsMa, Feng (Author) / Vittal, Vijay (Thesis advisor) / Tylavsky, Daniel (Committee member) / Heydt, Gerald (Committee member) / Si, Jennie (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the rapid growth of power systems and the concomitant technological advancements, the goal of achieving smart grids is no longer a vision but a foreseeable reality. Hence, the existing grids are undergoing infrastructural modifications to achieve the diverse characteristics of a smart grid. While there are many subjects associated with the operation of smart grids, this dissertation addresses two important aspects of smart grids: increased penetration of renewable resources, and increased reliance on sensor systems to improve reliability and performance of critical power system components. Present renewable portfolio standards are changing both structural and performance characteristics of power systems by replacing conventional generation with alternate energy resources such as photovoltaic (PV) systems. The present study investigates the impact of increased penetration of PV systems on steady state performance as well as transient stability of a large power system which is a portion of the Western U.S. interconnection. Utility scale and residential rooftop PVs are added to replace a portion of conventional generation resources. While steady state voltages are observed under various PV penetration levels, the impact of reduced inertia on transient stability performance is also examined. The simulation results obtained effectively identify both detrimental and beneficial impacts of increased PV penetration both for steady state stability and transient stability performance. With increased penetration of the renewable energy resources, and with the current loading scenario, more transmission system components such as transformers and circuit breakers are subject to increased stress and overloading. This research work explores the feasibility of increasing system reliability by applying condition monitoring systems to selected circuit breakers and transformers. A very important feature of smart grid technology is that this philosophy decreases maintenance costs by deploying condition monitoring systems that inform the operator of impending failures; or the approach can ameliorate problematic conditions. A method to identify the most critical transformers and circuit breakers with the aid of contingency ranking methods is presented in this study. The work reported in this dissertation parallels an industry sponsored study in which a considerable level of industry input and industry reported concerns are reflected.
ContributorsEftekharnejad, Sara (Author) / Heydt, Gerald (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Si, Jennie (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2012
Description
The combined heat and power (CHP)-based distributed generation (DG) or dis-tributed energy resources (DERs) are mature options available in the present energy mar-ket, considered to be an effective solution to promote energy efficiency. In the urban en-vironment, the electricity, water and natural gas distribution networks are becoming in-creasingly interconnected with the growing penetration of the CHP-based DG. Subse-quently, this emerging interdependence leads to new topics meriting serious consideration: how much of the CHP-based DG can be accommodated and where to locate these DERs, and given preexisting constraints, how to quantify the mutual impacts on operation performances between these urban energy distribution networks and the CHP-based DG. The early research work was conducted to investigate the feasibility and design methods for one residential microgrid system based on existing electricity, water and gas infrastructures of a residential community, mainly focusing on the economic planning. However, this proposed design method cannot determine the optimal DG sizing and sit-ing for a larger test bed with the given information of energy infrastructures. In this con-text, a more systematic as well as generalized approach should be developed to solve these problems. In the later study, the model architecture that integrates urban electricity, water and gas distribution networks, and the CHP-based DG system was developed. The pro-posed approach addressed the challenge of identifying the optimal sizing and siting of the CHP-based DG on these urban energy networks and the mutual impacts on operation per-formances were also quantified. For this study, the overall objective is to maximize the electrical output and recovered thermal output of the CHP-based DG units. The electrici-ty, gas, and water system models were developed individually and coupled by the devel-oped CHP-based DG system model. The resultant integrated system model is used to constrain the DG's electrical output and recovered thermal output, which are affected by multiple factors and thus analyzed in different case studies. The results indicate that the designed typical gas system is capable of supplying sufficient natural gas for the DG normal operation, while the present water system cannot support the complete recovery of the exhaust heat from the DG units.
ContributorsZhang, Xianjun (Author) / Karady, George G. (Thesis advisor) / Ariaratnam, Samuel T. (Committee member) / Holbert, Keith E. (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2013
Description
Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization.
ContributorsHe, Miao (Author) / Zhang, Junshan (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Hedman, Kory (Committee member) / Si, Jennie (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation considers an integrated approach to system design and controller design based on analyzing limits of system performance. Historically, plant design methodologies have not incorporated control relevant considerations. Such an approach could result in a system that might not meet its specifications (or one that requires a complex control architecture to do so). System and controller designers often go through several iterations in order to converge to an acceptable plant and controller design. The focus of this dissertation is on the design and control an air-breathing hypersonic vehicle using such an integrated system-control design framework. The goal is to reduce the number of system-control design iterations (by explicitly incorporate control considerations in the system design process), as well as to influence the guidance/trajectory specifications for the system. Due to the high computational costs associated with obtaining a dynamic model for each plant configuration considered, approximations to the system dynamics are used in the control design process. By formulating the control design problem using bilinear and polynomial matrix inequalities, several common control and system design constraints can be simultaneously incorporated into a vehicle design optimization. Several design problems are examined to illustrate the effectiveness of this approach (and to compare the computational burden of this methodology against more traditional approaches).
ContributorsSridharan, Srikanth (Author) / Rodriguez, Armando A (Thesis advisor) / Mittelmann, Hans D (Committee member) / Si, Jennie (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation includes two parts. First it focuses on discussing robust signal processing algorithms, which lead to consistent performance under perturbation or uncertainty in video target tracking applications. Projective distortion plagues the quality of long sequence mosaicking which results in loosing important target information. Some correction techniques require prior information. A new algorithm is proposed in this dissertation to this very issue. Optimization and parameter tuning of a robust camera motion estimation as well as implementation details are discussed for a real-time application using an ordinary general-purpose computer. Performance evaluations on real-world unmanned air vehicle (UAV) videos demonstrate the robustness of the proposed algorithms. The second half of the dissertation addresses neural signal analysis and modeling. Neural waveforms were recorded from rats' motor cortical areas while rats performed a learning control task. Prior to analyzing and modeling based on the recorded neural signal, neural action potentials are processed to detect neural action potentials which are considered the basic computation unit in the brain. Most algorithms rely on simple thresholding, which can be subjective. This dissertation proposes a new detection algorithm, which is an automatic procedure based on signal-to-noise ratio (SNR) from the neural waveforms. For spike sorting, this dissertation proposes a classification algorithm based on spike features in the frequency domain and adaptive clustering method such as the self-organizing map (SOM). Another major contribution of the dissertation is the study of functional interconnectivity of neurons in an ensemble. These functional correlations among neurons reveal spatial and temporal statistical dependencies, which consequently contributes to the understanding of a neuronal substrate of meaningful behaviors. This dissertation proposes a new generalized yet simple method to study adaptation of neural ensemble activities of a rat's motor cortical areas during its cognitive learning process. Results reveal interesting temporal firing patterns underlying the behavioral learning process.
ContributorsYang, Chenhui (Author) / Si, Jennie (Thesis advisor) / Jassemidis, Leonidas (Committee member) / Buneo, Christopher (Committee member) / Abousleman, Glen (Committee member) / Arizona State University (Publisher)
Created2012
Description
Contemporary methods for dynamic security assessment (DSA) mainly re-ly on time domain simulations to explore the influence of large disturbances in a power system. These methods are computationally intensive especially when the system operating point changes continually. The trajectory sensitivity method, when implemented and utilized as a complement to the existing DSA time domain simulation routine, can provide valuable insights into the system variation in re-sponse to system parameter changes. The implementation of the trajectory sensitivity analysis is based on an open source power system analysis toolbox called PSAT. Eight categories of sen-sitivity elements have been implemented and tested. The accuracy assessment of the implementation demonstrates the validity of both the theory and the imple-mentation. The computational burden introduced by the additional sensitivity equa-tions is relieved by two innovative methods: one is by employing a cluster to per-form the sensitivity calculations in parallel; the other one is by developing a mod-ified very dishonest Newton method in conjunction with the latest sparse matrix processing technology. The relation between the linear approximation accuracy and the perturba-tion size is also studied numerically. It is found that there is a fixed connection between the linear approximation accuracy and the perturbation size. Therefore this finding can serve as a general application guide to evaluate the accuracy of the linear approximation. The applicability of the trajectory sensitivity approach to a large realistic network has been demonstrated in detail. This research work applies the trajectory sensitivity analysis method to the Western Electricity Coordinating Council (WECC) system. Several typical power system dynamic security problems, in-cluding the transient angle stability problem, the voltage stability problem consid-ering load modeling uncertainty and the transient stability constrained interface real power flow limit calculation, have been addressed. Besides, a method based on the trajectory sensitivity approach and the model predictive control has been developed for determination of under frequency load shedding strategy for real time stability assessment. These applications have shown the great efficacy and accuracy of the trajectory sensitivity method in handling these traditional power system stability problems.
ContributorsHou, Guanji (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald (Committee member) / Tylavsky, Daniel (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2012
Description
Interictal spikes, together with seizures, have been recognized as the two hallmarks of epilepsy, a brain disorder that 1% of the world's population suffers from. Even though the presence of spikes in brain's electromagnetic activity has diagnostic value, their dynamics are still elusive. It was an objective of this dissertation to formulate a mathematical framework within which the dynamics of interictal spikes could be thoroughly investigated. A new epileptic spike detection algorithm was developed by employing data adaptive morphological filters. The performance of the spike detection algorithm was favorably compared with others in the literature. A novel spike spatial synchronization measure was developed and tested on coupled spiking neuron models. Application of this measure to individual epileptic spikes in EEG from patients with temporal lobe epilepsy revealed long-term trends of increase in synchronization between pairs of brain sites before seizures and desynchronization after seizures, in the same patient as well as across patients, thus supporting the hypothesis that seizures may occur to break (reset) the abnormal spike synchronization in the brain network. Furthermore, based on these results, a separate spatial analysis of spike rates was conducted that shed light onto conflicting results in the literature about variability of spike rate before and after seizure. The ability to automatically classify seizures into clinical and subclinical was a result of the above findings. A novel method for epileptogenic focus localization from interictal periods based on spike occurrences was also devised, combining concepts from graph theory, like eigenvector centrality, and the developed spike synchronization measure, and tested very favorably against the utilized gold rule in clinical practice for focus localization from seizures onset. Finally, in another application of resetting of brain dynamics at seizures, it was shown that it is possible to differentiate with a high accuracy between patients with epileptic seizures (ES) and patients with psychogenic nonepileptic seizures (PNES). The above studies of spike dynamics have elucidated many unknown aspects of ictogenesis and it is expected to significantly contribute to further understanding of the basic mechanisms that lead to seizures, the diagnosis and treatment of epilepsy.
ContributorsKrishnan, Balu (Author) / Iasemidis, Leonidas (Thesis advisor) / Tsakalis, Kostantinos (Committee member) / Spanias, Andreas (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2012
Description
The ability to plan, execute, and control goal oriented reaching and grasping movements is among the most essential functions of the brain. Yet, these movements are inherently variable; a result of the noise pervading the neural signals underlying sensorimotor processing. The specific influences and interactions of these noise processes remain unclear. Thus several studies have been performed to elucidate the role and influence of sensorimotor noise on movement variability. The first study focuses on sensory integration and movement planning across the reaching workspace. An experiment was designed to examine the relative contributions of vision and proprioception to movement planning by measuring the rotation of the initial movement direction induced by a perturbation of the visual feedback prior to movement onset. The results suggest that contribution of vision was relatively consistent across the evaluated workspace depths; however, the influence of vision differed between the vertical and later axes indicate that additional factors beyond vision and proprioception influence movement planning of 3-dimensional movements. If the first study investigated the role of noise in sensorimotor integration, the second and third studies investigate relative influence of sensorimotor noise on reaching performance. Specifically, they evaluate how the characteristics of neural processing that underlie movement planning and execution manifest in movement variability during natural reaching. Subjects performed reaching movements with and without visual feedback throughout the movement and the patterns of endpoint variability were compared across movement directions. The results of these studies suggest a primary role of visual feedback noise in shaping patterns of variability and in determining the relative influence of planning and execution related noise sources. The final work considers a computational approach to characterizing how sensorimotor processes interact to shape movement variability. A model of multi-modal feedback control was developed to simulate the interaction of planning and execution noise on reaching variability. The model predictions suggest that anisotropic properties of feedback noise significantly affect the relative influence of planning and execution noise on patterns of reaching variability.
ContributorsApker, Gregory Allen (Author) / Buneo, Christopher A (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Santello, Marco (Committee member) / Santos, Veronica (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2012
Description
This report provides an overview of scramjet-powered hypersonic vehicle modeling and control challenges. Such vehicles are characterized by unstable non-minimum phase dynamics with significant coupling and low thrust margins. Recent trends in hypersonic vehicle research are summarized. To illustrate control relevant design issues and tradeoffs, a generic nonlinear 3DOF longitudinal dynamics model capturing aero-elastic-propulsive interactions for wedge-shaped vehicle is used. Limitations of the model are discussed and numerous modifications have been made to address control relevant needs. Two different baseline configurations are examined over a two-stage to orbit ascent trajectory. The report highlights how vehicle level-flight static (trim) and dynamic properties change over the trajectory. Thermal choking constraints are imposed on control system design as a direct consequence of having a finite FER margin. The implication of this state-dependent nonlinear FER margin constraint, the right half plane (RHP) zero, and lightly damped flexible modes, on control system bandwidth (BW) and FPA tracking has been discussed. A control methodology has been proposed that addresses the above dynamics while providing some robustness to modeling uncertainty. Vehicle closure (the ability to fly a trajectory segment subject to constraints) is provided through a proposed vehicle design methodology. The design method attempts to use open loop metrics whenever possible to design the vehicle. The design method is applied to a vehicle/control law closed loop nonlinear simulation for validation. The 3DOF longitudinal modeling results are validated against a newly released NASA 6DOF code.
ContributorsDickeson, Jeffrey James (Author) / Rodriguez, Armando A (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Si, Jennie (Committee member) / Wells, Valana (Committee member) / Kawski, Mattias (Committee member) / Arizona State University (Publisher)
Created2012