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Classification of static security status using multi-class support vector machines
Published in Sultan Qaboos University
Volume: 9
Issue: 1
Pages: 21 - 30
This paper presents a Multi-class Support Vector Machine (SVM) based Pattern Recognition (PR) approach for static security assessment in power systems. The multi-class SVM classifier design is based on the calculation of a numeric index called the static security index. The proposed multi-class SVM based pattern recognition approach is tested on IEEE 57 Bus, 118 Bus and 300 Bus benchmark systems. The simulation results of the SVM classifier are compared to a Multilayer Perceptron (MLP) network and the Method of Least Squares (MLS). The SVM classifier was found to give high classification accuracy and a smaller misclassification rate compared to the other classifier techniques.
About the journal
JournalJournal of Engineering Research
PublisherSultan Qaboos University
Open AccessYes