Power Quality Event Classification Using Long Short-Term Memory Networks
| dc.contributor.author | Manikonda, S.K.G. | |
| dc.contributor.author | Santhosh, J. | |
| dc.contributor.author | Sreckala, S.P.K. | |
| dc.contributor.author | Gangwani, S. | |
| dc.contributor.author | Gaonkar, D.N. | |
| dc.date.accessioned | 2026-02-06T06:37:20Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Due 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. | |
| dc.identifier.citation | 2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2019 - Proceedings, 2019, Vol., , p. - | |
| dc.identifier.uri | https://doi.org/10.1109/DISCOVER47552.2019.9008009 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/31009 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | Classification | |
| dc.subject | Feature Extraction | |
| dc.subject | Long Short-Term Memory Networks | |
| dc.subject | Power Quality | |
| dc.subject | Recurrent Neural Network | |
| dc.title | Power Quality Event Classification Using Long Short-Term Memory Networks |