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1. Das, Swagata. Fault location and analysis in transmission and distribution networks.

Degree: PhD, Electrical and Computer Engineering, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/47202

Short-circuit faults are inevitable on transmission and distribution networks. In an effort to provide system operators with an accurate location estimate and reduce service restoration times, several impedance-based fault location algorithms have been developed for transmission and distribution networks. Each algorithm has specific input data requirements and make certain assumptions that may or may not hold true in a particular scenario. Identifying the best fault location approach, therefore, requires a thorough understanding of the working principle behind each algorithm. Moreover, impedance-based fault location algorithms require voltage and current phasors, captured by intelligent electronic devices (IEDs), to estimate the fault location. Unfortunately, voltage phasors are not always available due to operational constraints or equipment failure. Furthermore, impedance-based fault location algorithms assume a radial distribution feeder. With increased interconnection of distributed generators (DGs) to the feeder, this assumption is violated. DGs also contribute to the fault and severely compromise the accuracy of location estimates. In addition, the variability of certain DGs such as the fixed-speed wind turbine can alter fault current levels and result in relay misoperations. Finally, data recorded by IEDs during a fault contain a wealth of information and are prime for use in other applications that improve power system reliability. Based on the above background, the first objective of this dissertation is to present a comprehensive theory of impedance-based fault location algorithms. The contributions lie in clearly specifying the input data requirement of each algorithm and identifying their strengths and weaknesses. The following criteria are recommended for selecting the most suitable fault location algorithm: (a) data availability and (b) application scenario. The second objective is to develop fault location algorithms that use only the current to estimate the fault location. The simple but powerful algorithms allow system operators to locate faults even in the absence of voltage data. The third objective is to investigate the shortcomings of existing fault location algorithms when DGs are interconnected to the distribution feeder and develop an improved solution. A novel algorithm is proposed that require only the voltage and current phasors at the substation, is straightforward to implement, and is capable of locating all fault types. The fourth objective is to examine the effects of wind speed variation on the maximum and minimum fault current levels of a wind turbine and investigate the impact on relay settings. Contributions include developing an accurate time-domain model of a fixed-speed wind turbine with tower shadow and wind shear and verifying that the variation in wind speed does not violate relay settings calculated using the IEC 60909-0 Standard. The final objective is to exploit intelligent electronic device data for improving power system reliability. Contributions include validating the…
*Advisors/Committee Members: Santoso, Surya (advisor), Baldick, Ross (committee member), Becker, Michael F. (committee member), Hersh, Matt (committee member), Short, Thomas A. (committee member).*

Subjects/Keywords: Fault location; One-ended impedance-based methods; Two-ended impedance-based methods; Intelligent electronic devices; Power system faults; Power system reliability; Power quality; Event report analysis; Relay settings; Transmission line measurements; Distribution measurements; Impedance-measurement

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Das, S. (2015). Fault location and analysis in transmission and distribution networks. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47202

Chicago Manual of Style (16^{th} Edition):

Das, Swagata. “Fault location and analysis in transmission and distribution networks.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed June 06, 2020. http://hdl.handle.net/2152/47202.

MLA Handbook (7^{th} Edition):

Das, Swagata. “Fault location and analysis in transmission and distribution networks.” 2015. Web. 06 Jun 2020.

Vancouver:

Das S. Fault location and analysis in transmission and distribution networks. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2020 Jun 06]. Available from: http://hdl.handle.net/2152/47202.

Council of Science Editors:

Das S. Fault location and analysis in transmission and distribution networks. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/47202

University of Texas – Austin

2. Chang, Wanchen. Sufficient sample sizes for the multivariate multilevel regression model.

Degree: PhD, Educational Psychology, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/31009

The three-level multivariate multilevel model (MVMM) is a multivariate extension of the conventional univariate two-level hierarchical linear model (HLM) and is used for estimating and testing the effects of explanatory variables on a set of correlated continuous outcome measures. Two simulation studies were conducted to investigate the sample size requirements for restricted maximum likelihood (REML) estimation of three-level MVMMs, the effects of sample sizes and other design characteristics on estimation, and the performance of the MVMMs compared to corresponding two-level HLMs. The model for the first study was a random-intercept MVMM, and the model for the second study was a fully-conditional MVMM. Study conditions included number of clusters, cluster size, intraclass correlation coefficient, number of outcomes, and correlations between pairs of outcomes. The accuracy and precision of estimates were assessed with parameter bias, relative parameter bias, relative standard error bias, and 95% confidence interval coverage. Empirical power and type I error rates were also calculated. Implications of the results for applied researchers and suggestions for future methodological studies are discussed.
*Advisors/Committee Members: Beretvas, Susan Natasha (advisor), Pituch, Keenan A. (advisor), Hersh, Matt (committee member), Powers, Daniel (committee member), Whittaker, Tiffany (committee member).*

Subjects/Keywords: Multilevel modeling; Sample size

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Chang, W. (2015). Sufficient sample sizes for the multivariate multilevel regression model. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31009

Chicago Manual of Style (16^{th} Edition):

Chang, Wanchen. “Sufficient sample sizes for the multivariate multilevel regression model.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed June 06, 2020. http://hdl.handle.net/2152/31009.

MLA Handbook (7^{th} Edition):

Chang, Wanchen. “Sufficient sample sizes for the multivariate multilevel regression model.” 2015. Web. 06 Jun 2020.

Vancouver:

Chang W. Sufficient sample sizes for the multivariate multilevel regression model. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2020 Jun 06]. Available from: http://hdl.handle.net/2152/31009.

Council of Science Editors:

Chang W. Sufficient sample sizes for the multivariate multilevel regression model. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31009