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- Genre: Academic theses
- Creators: Davulcu, Hasan
- Creators: Ayyanar, Raja
- Creators: Johnston, Carol
- Resource Type: Text
- Status: Published
Description
Insulation aging monitoring is widely used to evaluate the operating condition of power equipment. One important monitoring method is detecting partial discharges (PD). PD is a localized breakdown of dielectric and its characteristics can give information about the insulation aging. Most existing test methods cannot identify different kinds of defects. Also, the practical application of PD detection in most existing test methods is restricted by weak PD signals and strong electric field disturbance from surroundings. In order to monitor aging situation in detail, types of PDs are important features to take into account. To classify different types of PDs, pulse sequence analysis (PSA) method is advocated to analyze PDs in the rod-plane model. This method can reflect cumulative effects of PDs, which are always ignored when only measuring PD value. It also shows uniform characteristics when different kinds of detecting system are utilized. Moreover, it does not need calibration. Analysis results from PSA show highly consistent distribution patterns for the same type of PDs and significant differences in the distribution patterns among types of PDs. Furthermore, a new method to detect PD signals using fiber bragg grating (FBG) based PD sensor is studied in this research. By using a piezoelectric ceramic transducer (PZT), small PD signals can be converted to pressure signal and then converted to an optical wavelength signal with FBG. The optical signal is isolated from the electric field; therefore its attenuation and anti-jamming performance will be better than traditional methods. Two sensors, one with resonant frequency of 42.7 kHz and the other 300 kHz, were used to explore the performance of this testing system. However, there were issues with the sensitivity of the sensors of these devices and the results have been communicated with the company. These devices could not give the results at the same level of accuracy as the conventional methods.
ContributorsCui, Longfei (Author) / Gorur, Ravi (Thesis advisor) / Vittal, Vijay (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2013
Description
The wide adoption and continued advancement of information and communications technologies (ICT) have made it easier than ever for individuals and groups to stay connected over long distances. These advances have greatly contributed in dramatically changing the dynamics of the modern day workplace to the point where it is now commonplace to see large, distributed multidisciplinary teams working together on a daily basis. However, in this environment, motivating, understanding, and valuing the diverse contributions of individual workers in collaborative enterprises becomes challenging. To address these issues, this thesis presents the goals, design, and implementation of Taskville, a distributed workplace game played by teams on large, public displays. Taskville uses a city building metaphor to represent the completion of individual and group tasks within an organization. Promising results from two usability studies and two longitudinal studies at a multidisciplinary school demonstrate that Taskville supports personal reflection and improves team awareness through an engaging workplace activity.
ContributorsNikkila, Shawn (Author) / Sundaram, Hari (Thesis advisor) / Byrne, Daragh (Committee member) / Davulcu, Hasan (Committee member) / Olson, Loren (Committee member) / Arizona State University (Publisher)
Created2013
Description
In modern electric power systems, energy management systems (EMSs) are responsi-ble for monitoring and controlling the generation system and transmission networks. State estimation (SE) is a critical `must run successful' component within the EMS software. This is dictated by the high reliability requirements and need to represent the closest real time model for market operations and other critical analysis functions in the EMS. Tradi-tionally, SE is run with data obtained only from supervisory control and data acquisition (SCADA) devices and systems. However, more emphasis on improving the performance of SE drives the inclusion of phasor measurement units (PMUs) into SE input data. PMU measurements are claimed to be more accurate than conventional measurements and PMUs `time stamp' measurements accurately. These widely distributed devices meas-ure the voltage phasors directly. That is, phase information for measured voltages and currents are available. PMUs provide data time stamps to synchronize measurements. Con-sidering the relatively small number of PMUs installed in contemporary power systems in North America, performing SE with only phasor measurements is not feasible. Thus a hy-brid SE, including both SCADA and PMU measurements, is the reality for contemporary power system SE. The hybrid approach is the focus of a number of research papers. There are many practical challenges in incorporating PMUs into SE input data. The higher reporting rates of PMUs as compared with SCADA measurements is one of the salient problems. The disparity of reporting rates raises a question whether buffering the phasor measurements helps to give better estimates of the states. The research presented in this thesis addresses the design of data buffers for PMU data as used in SE applications in electric power systems. The system theoretic analysis is illustrated using an operating electric power system in the southwest part of the USA. Var-ious instances of state estimation data have been used for analysis purposes. The details of the research, results obtained and conclusions drawn are presented in this document.
ContributorsMurugesan, Veerakumar (Author) / Vittal, Vijay (Committee member) / Heydt, Gerald (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2013
Description
A robust, fast and accurate protection system based on pilot protection concept was developed previously and a few alterations in that algorithm were made to make it faster and more reliable and then was applied to smart distribution grids to verify the results for it. The new 10 sample window method was adapted into the pilot protection program and its performance for the test bed system operation was tabulated. Following that the system comparison between the hardware results for the same algorithm and the simulation results were compared. The development of the dual slope percentage differential method, its comparison with the 10 sample average window pilot protection system and the effects of CT saturation on the pilot protection system are also shown in this thesis. The implementation of the 10 sample average window pilot protection system is done to multiple distribution grids like Green Hub v4.3, IEEE 34, LSSS loop and modified LSSS loop. Case studies of these multi-terminal model are presented, and the results are also shown in this thesis. The result obtained shows that the new algorithm for the previously proposed protection system successfully identifies fault on the test bed and the results for both hardware and software simulations match and the response time is approximately less than quarter of a cycle which is fast as compared to the present commercial protection system and satisfies the FREEDM system requirement.
ContributorsIyengar, Varun (Author) / Karady, George G. (Thesis advisor) / Ayyanar, Raja (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Most data cleaning systems aim to go from a given deterministic dirty database to another deterministic but clean database. Such an enterprise pre–supposes that it is in fact possible for the cleaning process to uniquely recover the clean versions of each dirty data tuple. This is not possible in many cases, where the most a cleaning system can do is to generate a (hopefully small) set of clean candidates for each dirty tuple. When the cleaning system is required to output a deterministic database, it is forced to pick one clean candidate (say the "most likely" candidate) per tuple. Such an approach can lead to loss of information. For example, consider a situation where there are three equally likely clean candidates of a dirty tuple. An appealing alternative that avoids such an information loss is to abandon the requirement that the output database be deterministic. In other words, even though the input (dirty) database is deterministic, I allow the reconstructed database to be probabilistic. Although such an approach does avoid the information loss, it also brings forth several challenges. For example, how many alternatives should be kept per tuple in the reconstructed database? Maintaining too many alternatives increases the size of the reconstructed database, and hence the query processing time. Second, while processing queries on the probabilistic database may well increase recall, how would they affect the precision of the query processing? In this thesis, I investigate these questions. My investigation is done in the context of a data cleaning system called BayesWipe that has the capability of producing multiple clean candidates per each dirty tuple, along with the probability that they are the correct cleaned version. I represent these alternatives as tuples in a tuple disjoint probabilistic database, and use the Mystiq system to process queries on it. This probabilistic reconstruction (called BayesWipe–PDB) is compared to a deterministic reconstruction (called BayesWipe–DET)—where the most likely clean candidate for each tuple is chosen, and the rest of the alternatives discarded.
ContributorsRihan, Preet Inder Singh (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
There has been a lot of research in the field of artificial intelligence about thinking machines. Alan Turing proposed a test to observe a machine's intelligent behaviour with respect to natural language conversation. The Winograd schema challenge is suggested as an alternative, to the Turing test. It needs inferencing capabilities, reasoning abilities and background knowledge to get the answer right. It involves a coreference resolution task in which a machine is given a sentence containing a situation which involves two entities, one pronoun and some more information about the situation and the machine has to come up with the right resolution of a pronoun to one of the entities. The complexity of the task is increased with the fact that the Winograd sentences are not constrained by one domain or specific sentence structure and it also contains a lot of human proper names. This modification makes the task of association of entities, to one particular word in the sentence, to derive the answer, difficult. I have developed a pronoun resolver system for the confined domain Winograd sentences. I have developed a classifier or filter which takes input sentences and decides to accept or reject them based on a particular criteria. Once the sentence is accepted. I run parsers on it to obtain the detailed analysis. Furthermore I have developed four answering modules which use world knowledge and inferencing mechanisms to try and resolve the pronoun. The four techniques I use are : ConceptNet knowledgebase, Search engine pattern counts,Narrative event chains and sentiment analysis. I have developed a particular aggregation mechanism for the answers from these modules to arrive at a final answer. I have used caching technique for the association relations that I obtain for different modules, so as to boost the performance. I run my system on the standard ‘nyu dataset’ of Winograd sentences and questions. This dataset is then restricted, by my classifier, to 90 sentences. I evaluate my system on this 90 sentence dataset. When I compare my results against the state of the art system on the same dataset, I get nearly 4.5 % improvement in the restricted domain.
ContributorsBudukh, Tejas Ulhas (Author) / Baral, Chitta (Thesis advisor) / VanLehn, Kurt (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis focuses on the influence of a grounded back electrode on the breakdown characteristics. The back electrode is an electrode which attaches at the back side of solid insulation. Insulation with grounded back electrode is a common type of insulation which is adopted in many high voltage power devices. While most of the power equipment work under AC voltage, most of the research on back electrode is focused on the DC voltage. Therefore, it is necessary to deeply investigate the influence of the back electrode under AC applied voltage. To investigate the influence of back electrode, the research is separated into two phases, which are the experiment phase and the electric field analysis phase. In the experiments, the breakdown voltages for both with and without back electrode are obtained. The experimental results indicate that the grounded back electrode does have impact on the breakdown characteristics. Then with the breakdown voltage, based on real experiment model, the electric field is analyzed using computer software. From the field simulation result, it is found that the back electrode also influences the electric field distribution. The inter relationship between the electric field and breakdown voltage is the key to explain all the results and phenomena observed during the experiment. Additionally, the influence of insulation barrier on breakdown is also investigated. Compared to the case without ground electrode, inserting a barrier into the gap can more significantly improve breakdown voltage.
ContributorsLiu, Jiajun (Author) / Karady, George G. (Thesis advisor) / Ayyanar, Raja (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Biological organisms are made up of cells containing numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted, manifesting as disease symptoms. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for activities including diagnosing diseases and drug development. Scientists studying these interconnected processes have identified various pathways involved in drug metabolism, diseases, and signal transduction, etc. High-throughput technologies, new algorithms and speed improvements over the last decade have resulted in deeper knowledge about biological systems, leading to more refined pathways. Such pathways tend to be large and complex, making it difficult for an individual to remember all aspects. Thus, computer models are needed to represent and analyze them. The refinement activity itself requires reasoning with a pathway model by posing queries against it and comparing the results against the real biological system. Many existing models focus on structural and/or factoid questions, relying on surface-level information. These are generally not the kind of questions that a biologist may ask someone to test their understanding of biological processes. Examples of questions requiring understanding of biological processes are available in introductory college level biology text books. Such questions serve as a model for the question answering system developed in this thesis. Thus, the main goal of this thesis is to develop a system that allows the encoding of knowledge about biological pathways to answer questions demonstrating understanding of the pathways. To that end, a language is developed to specify a pathway and pose questions against it. Some existing tools are modified and used to accomplish this goal. The utility of the framework developed in this thesis is illustrated with applications in the biological domain. Finally, the question answering system is used in real world applications by extracting pathway knowledge from text and answering questions related to drug development.
ContributorsAnwar, Saadat (Author) / Baral, Chitta (Thesis advisor) / Inoue, Katsumi (Committee member) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2014
Description
ABSTRACT Vitamin C plays an important role in fatty acid metabolism because it is required for carnitine synthesis. Vitamin C has been shown to have an inverse relationship with weight and body fat percent in a number of studies. However, there has been limited research exploring the relationship between vitamin C status and fat oxidation. This cross-sectional study investigates the relationship between plasma vitamin C and fat oxidation in 69 participants and between plasma vitamin C and body fatness in 82 participants. Participants were measured for substrate utilization via indirect calorimetry while at rest and measured for body fatness via DEXA scan. Participants provided a single fasting blood draw for analysis of plasma vitamin C. Results did not show a significant association between vitamin C and fat oxidation while at rest, therefore the data do not support the hypothesis that vitamin C status affects fat oxidation in a resting state. However, a significant inverse association was found between vitamin C and both total body fat percent and visceral fat.
ContributorsObermeyer, Lindsay (Author) / Johnston, Carol (Thesis advisor) / Hall, Rick (Committee member) / Swan, Pamela (Committee member) / Arizona State University (Publisher)
Created2014
Description
As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has sought out effective tools for their large-scale data analytics needs. Over this period, machine learning researchers have also been very prolific in designing improved algorithms which are capable of finding the hidden structure within these datasets. As consumers of popular Big Data frameworks have sought to apply and benefit from these improved learning algorithms, the problems encountered with the frameworks have motivated a new generation of Big Data tools to address the shortcomings of the previous generation. One important example of this is the improved performance in the newer tools with the large class of machine learning algorithms which are highly iterative in nature. In this thesis project, I set about to implement a low-rank matrix completion algorithm (as an example of a highly iterative algorithm) within a popular Big Data framework, and to evaluate its performance processing the Netflix Prize dataset. I begin by describing several approaches which I attempted, but which did not perform adequately. These include an implementation of the Singular Value Thresholding (SVT) algorithm within the Apache Mahout framework, which runs on top of the Apache Hadoop MapReduce engine. I then describe an approach which uses the Divide-Factor-Combine (DFC) algorithmic framework to parallelize the state-of-the-art low-rank completion algorithm Orthogoal Rank-One Matrix Pursuit (OR1MP) within the Apache Spark engine. I describe the results of a series of tests running this implementation with the Netflix dataset on clusters of various sizes, with various degrees of parallelism. For these experiments, I utilized the Amazon Elastic Compute Cloud (EC2) web service. In the final analysis, I conclude that the Spark DFC + OR1MP implementation does indeed produce competitive results, in both accuracy and performance. In particular, the Spark implementation performs nearly as well as the MATLAB implementation of OR1MP without any parallelism, and improves performance to a significant degree as the parallelism increases. In addition, the experience demonstrates how Spark's flexible programming model makes it straightforward to implement this parallel and iterative machine learning algorithm.
ContributorsKrouse, Brian (Author) / Ye, Jieping (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2014