Title :
Support vector machines for on-line security analysis of power systems
Author :
Cortés-Carmona, M. ; Jiménez-Estévez, G. ; Guevara-Cedeno, J.
Author_Institution :
Dept. of Electr. Eng., Univ. of Antofagasta, Antofagasta
Abstract :
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.
Keywords :
Bayes methods; pattern recognition; power engineering computing; power system security; support vector machines; Bayesian rule; IEEE test system; learning-based nonlinear classifier; on-line security analysis; pattern recognition approach; power systems; support vector machine; Decision support systems; Pattern recognition; Power system analysis computing; Power system dynamics; Power system security; Power system stability; Power system transients; Support vector machine classification; Support vector machines; Voltage; Neural networks; security assessment; support vector machines;
Conference_Titel :
Transmission and Distribution Conference and Exposition: Latin America, 2008 IEEE/PES
Conference_Location :
Bogota
Print_ISBN :
978-1-4244-2217-3
Electronic_ISBN :
978-1-4244-2218-0
DOI :
10.1109/TDC-LA.2008.4641770
