Certain hybrid machine learning approaches and converter models for mitigation of harmonic distortion in microgrid system

dc.contributor.guide	R, Maheswar
dc.coverage.spatial	Electrical Engineering
dc.creator.researcher	S, Gunasekaran
dc.date.accessioned	2019-03-14T07:29:11Z
dc.date.available	2019-03-14T07:29:11Z
dc.date.awarded	n.d.
dc.date.completed	2017
dc.date.registered	n.d.
dc.description.abstract	Abstract available
dc.description.note	Data not available
dc.format.accompanyingmaterial	None
dc.format.dimensions	30cm.
dc.format.extent	xxvi, 159p.
dc.identifier.uri	http://hdl.handle.net/10603/231691
dc.language	Others
dc.publisher.institution	Faculty of Electrical Engineering
dc.publisher.place	Chennai
dc.publisher.university	Anna University
dc.relation	143-158
dc.rights	university
dc.source.university	University
dc.subject.keyword	ActivePower
dc.subject.keyword	Converter
dc.subject.keyword	MachineLearning
dc.subject.keyword	Microgrid
dc.subject.keyword	Transistor
dc.title	Certain hybrid machine learning approaches and converter models for mitigation of harmonic distortion in microgrid system
dc.title.alternative
dc.type.degree	Ph.D.