Inrush transient generation and line impedance estimation
Name
1262873708-MIT.pdf
Size
24.39 MB
Format
Adobe PDF
Checksum (MD5)
e8b24981496a2ddf01d99bb73f689d7a
Author(s)
Saathoff, Erik K.
(Erik Karl)
Advisor(s)
Steven B. Leeb.
Date Issued
2021
Publisher
Massachusetts Institute of Technology
Abstract
An inrush transient contains extensive information that permits load identification, condition monitoring, and line impedance estimation. A power system monitor's (PSM) ability to identify a load based on its inrush behavior depends on the training exemplars used to create and optimize the load identification algorithm. This work discusses the use a phase-controlled switch that can be used in situ to integrate the effects of source and line impedance into the inrush data, and to generate transients at controllable turn-on phase angles relative to the voltage line-cycle. The resulting exemplars are more realistic than those generated with conventional techniques such as testing with an AC power supply. The control over angle also enables efficient investigation of a load's transient variability space. Testing loads in fault conditions expands the variability space, allowing load identification algorithms to correctly identify faulty loads and perform diagnostics. The large, high-frequency current that inrush transients inject into the line provides excellent excitation for line impedance estimation. Previous switching based approaches focus on fitting the line impedance to a model, i.e. parametric impedance estimation. This thesis extends previous work by providing the current excitation with common electrical loads rather than using capacitors, inductors, and short circuits. Non-parametric impedance estimation is also demonstrated. Inrush transients, and other transients generated by switching the load on and off rapidly, generate current with wide-bandwidth spectral content to replace previously used sinusoidal injection sweeps.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF version of thesis.
Includes bibliographical references (pages 195-198).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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