Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/15006
Full metadata record
DC FieldValueLanguage
dc.contributor.authorManikonda S.K.G.
dc.contributor.authorSanthosh J.
dc.contributor.authorSreekala S.P.K.
dc.contributor.authorGangwani S.
dc.contributor.authorGaonkar D.N.
dc.date.accessioned2021-05-05T10:16:11Z-
dc.date.available2021-05-05T10:16:11Z-
dc.date.issued2019
dc.identifier.citation2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2019 - Proceedings , Vol. , , p. -en_US
dc.identifier.urihttps://doi.org/10.1109/DISCOVER47552.2019.9008009
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/15006-
dc.description.abstractDue to the increased frequency of power quality events and complexity of modern electric grids, there is a growing need to classify such events. In this paper, a novel approach to the above problem has been explored, wherein Long Short-Term Memory networks have been employed to fulfil the power quality event classification task. Given the sheer size of the input dataset, feature extraction was carried out by deriving important statistical features from the data. The Long Short-Term Memory model used was then trained and tested on these extracted features. Following this, the model performance has been evaluated, wherein the model was shown to perform remarkably well. © 2019 IEEE.en_US
dc.titlePower Quality Event Classification Using Long Short-Term Memory Networksen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.