Electrical fault detection using machine learning algorithm.
In: AIP Conference Proceedings; 2023, Vol. 2782 Issue 1, p1-8, 8p; Jg. 2782 (2023-06-05) 1, S. 1-8
Konferenz
Zugriff:
Electrical fault detection is defined as the process of identifying a dataset's failure, such as power and current delivery. When Live wire contacts with Earth wire, it happens a short circuit. When a wires structure is interrupted due toa break in one of the wires (phase or neutral) or a fried fuse, an open circuit problem arises. A fault in a three-phase system can involve one or more phases and a ground failure, or it can happen just between the phases. Current flows into to the ground in a "ground fault" or "earth fault.". In most of the time, for a foreseeable fault, short current can be calculated. Electrical power system devices can detect faults and trip circuit breakers and other devices to reduce service interruptions in the event of faults. Simply identify data that needs to be cleaned before being analyzed. Unmonitored Electrical fault detection for unlabeled data is a method of detecting electric faults on both labeled and unlabeled data. Electrical defect detection is done according to the dataset in this project, and machine learning methods are employed on the dataset, withsimulation for improved prediction. Machine learning is increasingly being utilised to automate the identification of electrical faults. [ABSTRACT FROM AUTHOR]
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Titel: |
Electrical fault detection using machine learning algorithm.
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Autor/in / Beteiligte Person: | Vishal, Mehak ; Kamal, Aryan ; Viji, D. |
Quelle: | AIP Conference Proceedings; 2023, Vol. 2782 Issue 1, p1-8, 8p; Jg. 2782 (2023-06-05) 1, S. 1-8 |
Veröffentlichung: | 2023 |
Medientyp: | Konferenz |
ISSN: | 0094-243X (print) |
DOI: | 10.1063/5.0154682 |
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