Publication:
Dealing with ill conditioning in recursive parameter estimation for a synchronous generator

dc.contributor.advisor Vélez-Reyes, Miguel
dc.contributor.author Niño-Barón, Carlos E.
dc.contributor.college College of Engineering en_US
dc.contributor.committee O’Neill-Carrillo, Efraín
dc.contributor.committee Irrizary-Rivera, Agustín
dc.contributor.department Department of Electrical and Computer Engineering en_US
dc.contributor.representative Colucci-Ríos, José A
dc.date.accessioned 2019-05-15T17:59:36Z
dc.date.available 2019-05-15T17:59:36Z
dc.date.issued 2006
dc.description.abstract This thesis presents how to deal with ill-conditioning in recursive parameter estimation for a synchronous generator using subset selection, the Extended Kalman Filter (EKF), and the Iterated Extended Kalman Filter (IEKF). We present how the quality of the estimates in ill-conditioned parameter estimation problems is significantly affected by noise and how by proper modifications to the EKF, we still extract useful parameter estimates from low quality data. The modifications to the EKF and IEKF are based on the subset selection method, where only a subset of parameters is estimated from the available data and the other parameters are fixed to prior values. The reduced order parameter estimation problem is better conditioned allowing the extraction of good estimates from the available data. Simulation studies on the identification of a linearized model of a synchronous generator are used to illustrate the concepts being studied in this work. Simulation results show how the modifications to the EKF and IEKF based on the subset selection method result in convergent algorithms when their application to the original full problem was not. We also show that for this case the additional computational effort needed for the IEKF does not result in significant improvement in the quality of the estimates over those obtained with EKF. en_US
dc.description.abstract Esta tesis presenta los algoritmos de Filtro Extendido de Kalman (EKF), Filtro Iterado Extendido de Kalman (IEKF) usando subset selección para el manejo de mal acondicionamiento en identificación recursiva de parámetros de generadores sincrónicos. También es presentado como el EKF puede ser modificado para extraer información suficiente para calcular parámetros a partir de datos de baja calidad. La metodología propuesta se fundamenta en la sección de parámetros, donde un grupo de parámetros es fijado antes de realizar el proceso de estimación para reducir el mal acondicionamiento. Para mostrar los conceptos propuestos en este trabajo, fueron realizadas simulaciones empleando modelos linealizados de un generador sincrónico. Los resultados simulados muestran que el EKF de orden completo no converge bajo condiciones de ruido, sin embargo cuando se emplea la metodología de “subset selection” se pueden estimar 7 de 9 parámetros de forma confiable. en_US
dc.description.graduationYear 2006 en_US
dc.description.sponsorship Supported by the National Science Foundation under grant EEC- 0328200. This work made use of ERC Shared Facilities supported by the National Science Foundation under Award Number EEC-9731677. en_US
dc.identifier.uri https://hdl.handle.net/20.500.11801/2391
dc.language.iso English en_US
dc.rights.holder (c) 2006 Carlos Eduardo Niño-Barón en_US
dc.rights.license All rights reserved en_US
dc.title Dealing with ill conditioning in recursive parameter estimation for a synchronous generator en_US
dc.type Thesis en_US
dspace.entity.type Publication
thesis.degree.discipline Electrical Engineering en_US
thesis.degree.level M.S. en_US
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