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Electric utilities are exploring new technologies to cope up with the in-crease in electricity demand and power transfer capabilities of transmission lines. Compact transmission lines and high phase order systems are few of the techniques which enhance the power transfer capability of transmission lines without requiring any additional right-of-way. This

Electric utilities are exploring new technologies to cope up with the in-crease in electricity demand and power transfer capabilities of transmission lines. Compact transmission lines and high phase order systems are few of the techniques which enhance the power transfer capability of transmission lines without requiring any additional right-of-way. This research work investigates the impact of compacting high voltage transmission lines and high phase order systems on the surface electric field of composite insulators, a key factor deciding service performance of insulators. The electric field analysis was done using COULOMB 9.0, a 3D software package which uses a numerical analysis technique based on Boundary Element Method (BEM). 3D models of various types of standard transmission towers used for 230 kV, 345 kV and 500 kV level were modeled with different insulators con-figurations and number of circuits. Standard tower configuration models were compacted by reducing the clearance from live parts in steps of 10%. It was found that the standard tower configuration can be compacted to 30% without violating the minimum safety clearance mandated by NESC standards. The study shows that surface electric field on insulators for few of the compact structures exceeded the maximum allowable limit even if corona rings were installed. As a part of this study, a Gaussian process model based optimization pro-gram was developed to find the optimum corona ring dimensions to limit the electric field within stipulated values. The optimization program provides the dimen-sions of corona ring, its placement from the high voltage end for a given dry arc length of insulator and system voltage. JMP, a statistical computer package and AMPL, a computer language widely used form optimization was used for optimi-zation program. The results obtained from optimization program validated the industrial standards.
ContributorsMohan, Nihal (Author) / Gorur, Ravi S. (Thesis advisor) / Heydt, Gerald T. (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2012
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Description
This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important

This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important and growing area of signal processing research over the past decade. Here, we explore the application of statistical modeling and signal processing concepts to data obtained from the Global Group Relations Project, specifically to understand and quantify the effects and interactions of social psychological factors related to intergroup conflicts. We use Bayesian networks to specify prospective models of conditional dependence. Bayesian networks are determined between social psychological factors and conflict variables, and modeled by directed acyclic graphs, while the significant interactions are modeled as conditional probabilities. Since the data are sparse and multi-dimensional, we regress Gaussian mixture models (GMMs) against the data to estimate the conditional probabilities of interest. The parameters of GMMs are estimated using the expectation-maximization (EM) algorithm. However, the EM algorithm may suffer from over-fitting problem due to the high dimensionality and limited observations entailed in this data set. Therefore, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are used for GMM order estimation. To assist intuitive understanding of the interactions of social variables and the intergroup conflicts, we introduce a color-based visualization scheme. In this scheme, the intensities of colors are proportional to the conditional probabilities observed.
ContributorsLiu, Hui (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2012
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Description
In this work, I propose to bridge the gap between human users and adaptive control of robotic systems. The goal is to enable robots to consider user feedback and adjust their behaviors. A critical challenge with designing such systems is that users are often non-experts, with limited knowledge about

In this work, I propose to bridge the gap between human users and adaptive control of robotic systems. The goal is to enable robots to consider user feedback and adjust their behaviors. A critical challenge with designing such systems is that users are often non-experts, with limited knowledge about the robot's hardware and dynamics. In the domain of human-robot interaction, there exist different modalities of conveying information regarding the desired behavior of the robot, most commonly used are demonstrations, and preferences. While it is challenging for non-experts to provide demonstrations of robot behavior, works that consider preferences expressed as trajectory rankings lead to users providing noisy and possibly conflicting information, leading to slow adaptation or system failures. The end user can be expected to be familiar with the dynamics and how they relate to their desired objectives through repeated interactions with the system. However, due to inadequate knowledge about the system dynamics, it is expected that the user would find it challenging to provide feedback on all dimension's of the system's behavior at all times. Thus, the key innovation of this work is to enable users to provide partial instead of completely specified preferences as with traditional methods that learn from user preferences. In particular, I consider partial preferences in the form of preferences over plant dynamic parameters, for which I propose Adaptive User Control (AUC) of robotic systems. I leverage the correlations between the observed and hidden parameter preferences to deal with incompleteness. I use a sparse Gaussian Process Latent Variable Model formulation to learn hidden variables that represent the relationships between the observed and hidden preferences over the system parameters. This model is trained using Stochastic Variational Inference with a distributed loss formulation. I evaluate AUC in a custom drone-swarm environment and several domains from DeepMind control suite. I compare AUC with the state-of-the-art preference-based reinforcement learning methods that are utilized with user preferences. Results show that AUC outperforms the baselines substantially in terms of sample and feedback complexity.
ContributorsBiswas, Upasana (Author) / Zhang, Yu (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Berman, Spring (Committee member) / Liu, Lantao (Committee member) / Arizona State University (Publisher)
Created2023