Classification of Static Security Status Using Multi-Class Support Vector Machines

S Kalyani, KS Swarup

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

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References


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DOI: http://dx.doi.org/10.24200/tjer.vol9iss1pp21-30

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