Name
Title | ||
|---|---|---|
An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events |
Abstract | ||
|---|---|---|
•Classification of unknown composite PQD variations with high performance using known PQD variations.•Development of an adaptive CNN architecture that is responsive to different numbers of IMF inputs.•Flexible architecture is suitable for working with different signal processing methods such as EMD and VMD.•High classification performance compared to current state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2021.115023 | Expert Systems with Applications |
Keywords | DocType | Volume |
Power quality disturbance (PQD),Deep learning,CNN,Classification,Signal monitoring,Signal disturbance | Journal | 178 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hatem Sindi | 1 | 3 | 1.76 |
| Majid Nour | 2 | 3 | 1.08 |
| Muhyaddin J. H. Rawa | 3 | 0 | 2.03 |
| Saban Ozturk | 4 | 15 | 5.42 |
| Kemal Polat | 5 | 1348 | 97.38 |