Measurement and Modeling for Resource Monitoring
Name
ponce-eaponce-phd-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
79.41 MB
Format
Adobe PDF
Checksum (MD5)
524b6683681c31b1b9001ffe9e6f3847
Author(s)
Ponce, Eric Andrew
Advisor(s)
Leeb, Steven B.
Date Issued
September 2023
Publisher
Massachusetts Institute of Technology
Abstract
Effectively tracking resource consumption through ``smart'' metering provides value. Installing such meters, however, is costly, labor-intensive, and could potentially disrupt sensitive, aging distribution networks. Utilizing existing ``analog'' meters through noninvasive retrofit methods provides a more feasible solution to transform our networks into ``smart'' ones by significantly reducing material and installation costs. The high-quality flow rate data produced by these retrofits also enables nonintrusive load monitoring (NILM) for applications such as condition-based maintenance.
Distributed sensing technologies such as those used for resource tracking require electrical power that may not be easily available at the installation site. The toroidal current transformer based magnetic energy harvester (CTMEH), which leverages the existence of extensive electrical power grid cabling, provides a potential solution. Its usefulness, however, is limited by the need to thread the power conductor through the toroidal transformer.
A model for a CTMEH using a split-core toroid is necessary to enable the design of more useful energy harvesting mechanisms necessary for distributed sensing technologies.
The spread of constant power loads (CPLs) connected to rectifiers in power distribution systems poses a potential stability problem that requires a means of analyzing stability under different source and load impedance conditions. Stability analysis methods allow for the design of ``smart'' loads that can dynamically adjust their operation to prevent instability. Investigation of the currents and voltages in such systems would also aid NILM efforts.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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