An Enhanced Signal Processing Toolbox for Electrical Energy Monitoring
Name
langham-alangham-ee-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
17.89 MB
Format
Adobe PDF
Checksum (MD5)
227b82f7b5d8afbbc2b2ac8301bca714
Author(s)
Langham, Aaron William
Advisor(s)
Leeb, Steven B.
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
A nonintrusive load monitor (NILM) aims to perform power system analysis with a minimally invasive sensor profile. A wealth of literature exists for load identification and energy disaggregation under ideal, healthy conditions. However, a significant value proposition of nonintrusive load monitoring comes from fault detection and diagnostics. Early detection of electromechanical faults aids safety, reduces energy waste, and saves money. However, load identification and energy disaggregation are complicated by faulty or time-varying load operation profiles. This thesis extends previous thesis work by the author that addresses this issue. A new, “multistream” feature extraction approach to nonintrusive power monitoring is presented. This approach enables targeted electrical data analysis on non-stationary electrical systems.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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