Contingency evaluation and monitorization using artificial neural networks.

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Bibliographic Details
Title:	Contingency evaluation and monitorization using artificial neural networks.
Authors:	Joya, G.¹ joya@dte.uma.es, García-Lagos, Francisco¹ fgl@uma.es, Sandoval, F.¹ sandoval@dte.uma.es
Source:	Neural Computing & Applications. Feb2010, Vol. 19 Issue 1, p139-150. 12p. 1 Diagram, 6 Charts, 4 Graphs.
Subjects:	Artificial neural networks, Backup processing alternatives in electronic data processing, Self-organizing maps, Visualization, Artificial intelligence
Abstract:	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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	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. [ABSTRACT FROM AUTHOR]
ISSN:	09410643
DOI:	10.1007/s00521-009-0267-x