Contingency evaluation and monitorization using artificial neural networks
| Autor: | Francisco García-Lagos, Gonzalo Joya, Francisco Sandoval |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Self-organizing map
Artificial neural network business.industry Computer science Computer Science::Neural and Evolutionary Computation Machine learning computer.software_genre Level set Models of neural computation Ranking Artificial Intelligence Multilayer perceptron Radial basis function Artificial intelligence business computer Software |
| Zdroj: | Neural Computing and Applications. 19:139-150 |
| ISSN: | 1433-3058 0941-0643 |
| Popis: | In this paper, different neural network-based solutions to the contingency analysis problem are presented. Contingency analysis is examined from two perspectives: as a functional approximation problem obtaining a numerical evaluation and ranking contingencies; and as a graphical monitoring problem, obtaining an easy visualization system of the relative severity of the contingencies. For the functional evaluation problem, we analyze the use of different supervised feed-forward artificial neural networks (multilayer perceptron and radial basis function networks). The proposed systems produce a very accurate evaluation and ranking, and so present a high applicability. For the graphical monitoring problem, unsupervised artificial neural networks such as self-organizing maps by Kohonen have been used. This solution allows both a rapid, easy and simultaneous visualization of the severity level of the complete contingency set. The proposed solutions avoid the main drawbacks of previous neural network approaches to this problem, which are explicitly analyzed here. |
| Databáze: | OpenAIRE |
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