Name
Abstract | ||
|---|---|---|
Active filters remove harmonic current by pouring in a compensation current which is equal to the quantity of harmonic current with opposite sign. They require a high performance harmonic analyzer. Recurrent neural networks (RNN) have the ability of conversion without affecting phase change. They also learn how to convert load current to fundamental current by themselves. These abilities enable RNN to be applied to the harmonic current analyzer of active filters. We suggest such a use for RNN and investigate their ability to eliminate harmonics. We show that RNN can eliminate harmonic current without being influenced by the composition rate and phase of the harmonic current and that they can work as a high performance harmonic analyzer |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/ICNN.1995.488225 | Neural Networks, 1995. Proceedings., IEEE International Conference |
Keywords | Field | DocType |
active filters,harmonic analysis,learning (artificial intelligence),neural chips,power electronics,power system harmonics,recurrent neural nets,active filter,compensation current,harmonic current analyzer,harmonic current removal,high performance harmonic analyzer,load current conversion,recurrent neural network,phase change,learning artificial intelligence | Active filter,Phase change,Computer science,Control theory,Harmonic,Recurrent neural network,Harmonic analysis,Harmonics,Power electronics,Spectrum analyzer,Distributed computing | Conference |
Volume | ISBN | Citations |
1 | 0-7803-2768-3 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yoshiko wada | 1 | 0 | 0.34 |
| Narade Pecharanin | 2 | 5 | 4.49 |
| atsushi taguchi | 3 | 0 | 0.34 |
| Nobukazu Iijima | 4 | 1 | 1.11 |
| y akima | 5 | 0 | 0.34 |
| Mototaka Sone | 6 | 5 | 5.50 |