FAULT IDENTIFICATION USING NEURAL NETWORKS

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DC Field	Value	Language
dc.contributor.author	Rangisetti, Praveen	-
dc.date.accessioned	2014-09-29T05:37:36Z	-
dc.date.available	2014-09-29T05:37:36Z	-
dc.date.issued	2007	-
dc.identifier	M.Tech	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2754	-
dc.guide	Pillai, G. N.	-
dc.description.abstract	In this thesis the aim is to detect the high impedance fault occurring on radial electrical distribution systems using neural network based relaying scheme. A multilayer perceptron is used for distinguishing the linear and nonlinear High Impedance Faults by taking the Feature vector as input. R.M.S values of third and fifth harmonic components of feeder voltage and feeder current are used as the feature vector obtained by applying the Fast Fourier Transform on the Feeder voltage and Feeder current.	en_US
dc.language.iso	en	en_US
dc.subject	ELECTRICAL ENGINEERING	en_US
dc.subject	NEURAL NETWORKS	en_US
dc.subject	FAULT IDENTIFICATION	en_US
dc.subject	ARTIFICIAL INTELLIGENCE	en_US
dc.title	FAULT IDENTIFICATION USING NEURAL NETWORKS	en_US
dc.type	M.Tech Dessertation	en_US
dc.accession.number	613071	en_US
Appears in Collections:	MASTERS' THESES (Electrical Engg)

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