2026-05-21T13:38:44Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1877482024-12-23T18:01:48Zoai_pmh:alloai_pmh:repo_items187748
https://hdl.handle.net/2286/R.2.N.187748
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All Rights Reserved
2023
44 pages
Masters Thesis
Academic theses
Text
eng
Ghosh, Kinjal
Weng, Yang
Pal, Anamitra
Hedman, Mojdeh K
Arizona State University
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Electrical Engineering
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.
Energy
Active Learning algorithm
incipient fault
MATLAB Simulink
Python
Active Learning for Incipient Fault Detection