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dc.contributor.advisorVélez-Reyes, Miguel
dc.contributor.authorNiño-Barón, Carlos E.
dc.date.accessioned2019-05-15T17:59:36Z
dc.date.available2019-05-15T17:59:36Z
dc.date.issued2006
dc.identifier.urihttps://hdl.handle.net/20.500.11801/2391
dc.description.abstractThis 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.abstractEsta 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.sponsorshipSupported 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.language.isoEnglishen_US
dc.titleDealing with ill conditioning in recursive parameter estimation for a synchronous generatoren_US
dc.typeThesisen_US
dc.rights.licenseAll rights reserveden_US
dc.rights.holder(c) 2006 Carlos Eduardo Niño-Barónen_US
dc.contributor.committeeO’Neill-Carrillo, Efraín
dc.contributor.committeeIrrizary-Rivera, Agustín
dc.contributor.representativeColucci-Ríos, José A
thesis.degree.levelM.S.en_US
thesis.degree.disciplineElectrical Engineeringen_US
dc.contributor.collegeCollege of Engineeringen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.description.graduationYear2006en_US


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    Items included under this collection are theses, dissertations, and project reports submitted as a requirement for completing a graduate degree at UPR-Mayagüez.

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