Full metadata
Title
Active Learning for Incipient Fault Detection
Description
Fault detection is an integral part for power systems as without its proper study, analysis and mitigation, people will not be able to power the various appliances and equipment required in all aspects of life. One such type of fault which is very criticalin an electrical cable but very difficult to spot is incipient fault. These momentary faults are observed for very short periods however, if it persists, this would lead to consequences like insulation wear-off and even, power outages. With the advent of
machine learning in the power systems fraternity, this paper also uses a new and updated Active Learning algorithm to detect incipient fault data from a simulated test case. The objective of the paper is to detect the fault data accurately using this new and precise method. For purposes of data collection and training of the model, MATLAB Simulink and Python programming has been used respectively.
Date Created
2023
Contributors
- Ghosh, Kinjal (Author)
- Weng, Yang (Thesis advisor)
- Pal, Anamitra (Committee member)
- Hedman, Mojdeh K (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
44 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.187748
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Electrical Engineering
System Created
- 2023-06-07 12:21:45
System Modified
- 2023-06-07 12:21:50
- 1 year 5 months ago
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