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Abstract

 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.

 

Keywords

Static security Classifier Multi-class SVM Pattern recognition

Article Details

How to Cite
Kalyani, S., & Swarup, K. (2012). Classification of Static Security Status Using Multi-Class Support Vector Machines. The Journal of Engineering Research [TJER], 9(1), 21–30. https://doi.org/10.24200/tjer.vol9iss1pp21-30
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