Name
Abstract | ||
|---|---|---|
In this paper an analysis of the applicability of different neural paradigms to contingency analysis in power systems is presented. On one hand, unsupervised Self-Organizing Maps by Kohonen have been implemented for visualization and graphic monitoring of contingency severity. On the other hand, supervised feed-forward neural paradigms such as Multilayer Perceptron and Radial Basis Function, are implemented for severity numerical evaluation and contingency ranking. Experiments have been performed with successfully result in the case of Kohonen and Multilayer Perceptron paradigms. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-45723-2_86 | IWANN (2) |
Keywords | Field | DocType |
different neural paradigm,graphic monitoring,power systems,multilayer perceptron,contingency ranking,contingency severity,contingency evaluation,neural networks,severity numerical evaluation,radial basis function,multilayer perceptron paradigm,supervised feed-forward neural,contingency analysis,power system,neural network | Radial basis function,Computer science,Visualization,Electric power system,Self-organizing map,Multilayer perceptron,Artificial intelligence,Artificial neural network,Machine learning,Contingency,Feed forward | Conference |
ISBN | Citations | PageRank |
3-540-42237-4 | 1 | 0.42 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. García | 1 | 27 | 7.39 |
| Gonzalo Joya Caparrós | 2 | 175 | 19.70 |
| Francisco Javier Marín | 3 | 2 | 1.17 |
| Francisco Sandoval Hernández | 4 | 771 | 104.15 |