dc.contributor.advisor | Jeffrey Lang. | en_US |
dc.contributor.author | Casallas, Alan(Alan E.) | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
dc.date.accessioned | 2020-03-24T15:35:39Z | |
dc.date.available | 2020-03-24T15:35:39Z | |
dc.date.copyright | 2019 | en_US |
dc.date.issued | 2019 | en_US |
dc.identifier.uri | https://hdl.handle.net/1721.1/124235 | |
dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 | en_US |
dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (pages 329-330). | en_US |
dc.description.abstract | This 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.statementofresponsibility | by Alan Casallas. | en_US |
dc.format.extent | 330 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | MIT 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.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Electrical Engineering and Computer Science. | en_US |
dc.title | Contactless voltage and current estimation using signal processing and machine learning | en_US |
dc.type | Thesis | en_US |
dc.description.degree | M. Eng. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.identifier.oclc | 1144933918 | en_US |
dc.description.collection | M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
dspace.imported | 2020-03-24T15:35:37Z | en_US |
mit.thesis.degree | Master | en_US |
mit.thesis.department | EECS | en_US |