Name
Abstract | ||
|---|---|---|
A back-propagation neural network method is proposed for accurate frequencies, amplitudes and phases estimation from periodic signals in power systems, and the convergence theorem shows that the proposed algorithm can be convergent asymptotically to its global minimum. The method is aimed at the system in which the sampling frequency cannot be locked on the actual fundamental frequency. Some simulating examples are given and the results show that the accuracy of the estimates provided by the proposed approach in the asynchronous case is relatively better than that of the estimates obtained with the conventional harmonic analysis methods. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72395-0_123 | ISNN (3) |
Keywords | Field | DocType |
back-propagation neural network method,accurate frequency,actual fundamental frequency,neural network,convergent asymptotically,asynchronous case,conventional harmonic analysis method,sampling frequency,high accuracy frequency harmonic,power system,convergence theorem,proposed algorithm,harmonic analysis,fundamental frequency | Convergence (routing),Fundamental frequency,Control theory,Computer science,Sampling (signal processing),Electric power system,Harmonic analysis,Artificial neural network,Periodic graph (geometry),Amplitude | Conference |
Volume | ISSN | Citations |
4493 | 0302-9743 | 1 |
PageRank | References | Authors |
0.63 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-hua Wang | 1 | 15 | 2.84 |
| Yi-gang He | 2 | 339 | 43.21 |
| LONG Ying | 3 | 30 | 9.46 |