Machine learning framework for power system fault detection and classification using convolutional neural networks and long short term memory

Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/314452

Full metadata record

DC Field	Value	Language
dc.coverage.spatial	Electronic engineering
dc.date.accessioned	2021-02-05T11:31:11Z	-
dc.date.available	2021-02-05T11:31:11Z	-
dc.identifier.uri	http://hdl.handle.net/10603/314452	-
dc.description.abstract	file attached
dc.format.extent	112 p.
dc.language	English
dc.relation	no. of references 124
dc.rights	university
dc.title	Machine learning framework for power system fault detection and classification using convolutional neural networks and long short term memory
dc.title.alternative	-
dc.creator.researcher	D Baskar
dc.subject.keyword	Engineering and Technology
dc.subject.keyword	Engineering
dc.subject.keyword	Engineering Electrical and Electronic
dc.subject.keyword	Machine learning
dc.subject.keyword	Artificial intelligence
dc.subject.keyword	Machine theory
dc.subject.keyword	Electric fault location
dc.subject.keyword	Electric fault location--Data processing
dc.description.note	references p.97-112
dc.contributor.guide	P Selvam
dc.publisher.place	Salem
dc.publisher.university	Vinayaka Missions University
dc.publisher.institution	Department of Electrical and Electronics Engineering
dc.date.registered
dc.date.completed	2020
dc.date.awarded
dc.format.dimensions	-
dc.format.accompanyingmaterial	None
dc.type.degree	Ph.D.
dc.source.inflibnet	INFLIBNET
Appears in Departments:	Department of Electrical and Electronics Engineering

Files in This Item:

File	Description	Size	Format
01_title.pdf	Attached File	51.09 kB	Adobe PDF	View/Open
02_declaration.pdf		826.59 kB	Adobe PDF	View/Open
03_certificate.pdf		1.35 MB	Adobe PDF	View/Open
04_acknowledgement.pdf		33.39 kB	Adobe PDF	View/Open
05_abstract.pdf		35.22 kB	Adobe PDF	View/Open
06_content.pdf		67.97 kB	Adobe PDF	View/Open
07_chapter 1.pdf		278.22 kB	Adobe PDF	View/Open
08_chapter 2.pdf		762.18 kB	Adobe PDF	View/Open
09_chapter 3.pdf		1.19 MB	Adobe PDF	View/Open
10_chapter 4.pdf		700.18 kB	Adobe PDF	View/Open
11_chapter 5.pdf		318.19 kB	Adobe PDF	View/Open
12_references.pdf		131.81 kB	Adobe PDF	View/Open
13_annexure.pdf		1.96 MB	Adobe PDF	View/Open
80_recommendation.pdf		80.5 kB	Adobe PDF	View/Open

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