Contactless voltage and current estimation using signal processing and machine learning

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 329-330).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.by Alan Casallas.M. Eng.M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc