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dc.contributor.advisorJeffrey Lang.en_US
dc.contributor.authorCasallas, Alan(Alan E.)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2020-03-24T15:35:39Z
dc.date.available2020-03-24T15:35:39Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/124235
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 329-330).en_US
dc.description.abstractThis thesis describes a contactless sensor developed to estimate the line currents and line-to-line voltages of a multi-phase cable in the presence of significant external disturbances. The current estimates are derived from an array of point magnetic-field measurements processed by a linear least-square-error estimator. The gains in the estimator are chosen using a probabilistic model of measurement errors created by external magnetic field sources. Test bed validation of the estimates demonstrates estimation errors below 1% even in the presence of nearby cables carrying comparable currents, metal plates that could support eddy currents, and large magnetizable cores. The voltage estimates are derived using actively-guarded electrodes that capacitively couple to the cable conductors. Knowing the coupling capacitance, test bed validation of the estimates again demonstrates estimation errors below 1% even in the presence of nearby cables carrying comparable voltages, and metal plates. A method involving capacitively coupling signals onto the cables is also proposed and demonstrated to determine the coupling capacitance without operator intervention.en_US
dc.description.statementofresponsibilityby Alan Casallas.en_US
dc.format.extent330 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleContactless voltage and current estimation using signal processing and machine learningen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1144933918en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2020-03-24T15:35:37Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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