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Massachusetts Institute of Technology

Electrical Monitoring of Electromechanical Systems

Abstract

dc:description.abstract

Electromechanical systems provide the world’s backbone for generating and using energy. Electromechanical systems can also experience an innumerable set of failures, causing induced wear and wasted energy, or eventually a complete failure of a critical piece of equipment or system. Degradation or other faults are often associated with subtle but observable changes in electrical consumption. A nonintrusive load monitor (NILM) is a convenient tool for electrical monitoring, in which all loads connected downstream of an electrical panel are monitored with a single set of current and voltage sensors. If collated in a useful way, nonintrusive electrical data can make diagnostic information more easily attainable and improve the efficient operation of critical machines. Ensuring correct nonintrusive identification of load operation is a challenge in varying operating conditions and fault scenarios. Most nonintrusive load monitoring research assumes that data is static over time. Also, ground truth labels are a scarce resource in industrial scenarios. Thus, a pattern classifier must train on a limited dataset not representative of long-term operation. This thesis employs an understanding of the physics and time-dependency behind changing load behavior to inform pattern classification. New statistical feature extraction techniques are presented for loads with time-varying operation. Results are demonstrated with laboratory experiments and case-studies from NILM installations onboard various marine microgrids.

Degree

thesis:*
Name thesis:degree_name
Doctoral
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Green, Daisy Hikari
Advisors dc:contributor.advisor
  • Leeb, Steven B.
  • Donnal, John S.

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright MIT

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/144801
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/144801

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Green, Daisy Hikari. Electrical Monitoring of Electromechanical Systems. Massachusetts Institute of Technology, 2022. https://hdl.handle.net/1721.1/144801