Massachusetts Institute of Technology
Electrical Monitoring of Electromechanical Systems
Abstract
dc:description.abstractElectromechanical 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
- Licence dc:rights.uri
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