Neural Networks for Contingency Evaluation and Monitoring in Power Systems
| Autor: | Francisco Sandoval Hernández, Gonzalo Joya Caparrós, Francisco García-Lagos, F.J. Marin |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Self-organizing map
Electric power system ComputingMethodologies_PATTERNRECOGNITION Artificial neural network Computer science business.industry Multilayer perceptron Computer Science::Neural and Evolutionary Computation Feed forward Radial basis function Artificial intelligence Contingency business |
| Zdroj: | Bio-Inspired Applications of Connectionism ISBN: 9783540422372 IWANN (2) |
| DOI: | 10.1007/3-540-45723-2_86 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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