Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/24491| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Zlatkova, Aleksandra | en_US |
| dc.contributor.author | Markovska, Marija | en_US |
| dc.contributor.author | Taskovski, Dimitar | en_US |
| dc.date.accessioned | 2022-11-21T09:12:51Z | - |
| dc.date.available | 2022-11-21T09:12:51Z | - |
| dc.date.issued | 2022-06-28 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.12188/24491 | - |
| dc.publisher | IEEE | en_US |
| dc.title | Deep learning approach for classification of PQ disturbances | en_US |
| dc.type | Proceeding article | en_US |
| dc.relation.conference | 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) | en_US |
| dc.identifier.doi | 10.1109/eeeic/icpseurope54979.2022.9854673 | - |
| dc.identifier.url | http://xplorestaging.ieee.org/ielx7/9854509/9854408/09854673.pdf?arnumber=9854673 | - |
| item.grantfulltext | none | - |
| item.fulltext | No Fulltext | - |
| Appears in Collections: | Faculty of Electrical Engineering and Information Technologies: Conference Papers | |
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