Support vector machines for on-line security analysis of power systems

Cortes-Carmona, M.; Jimenez-Estevez, G.; Guevara-Cedeno, J.

Keywords: security, power systems, support vector machines


The pattern recognition approach for security analysis (SA) of power systems has been presented as a promising tool for on-line applications. This paper applies a learning-based nonlinear classifier, which is a support vector machine (SVM) for SA. Three single SVM are trained to classify the state of the system: secure, alert and emergency. The final classification is obtained combining the output of each classifier with a Bayesian rule. The effectiveness of the proposed approach has been demonstrated on two IEEE test systems.

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Fecha de publicación: 2008
Año de Inicio/Término: 13-15 Aug. 2008
Página de inicio: 1
Página final: 6