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

S Kalyani, Department of Electrical and Electronics Engineering, Kamaraj College of Engineering & Technology, Tamilnadu, India
KS Swarup, Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai - 600036, India

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.

Recommended Citation

Kalyani, S and Swarup, KS (2012) "Classification of Static Security Status Using Multi-Class Support Vector Machines," The Journal of Engineering Research: Vol. 9: Iss. 1, Article 3.
DOI: https://doi.org/10.24200/tjer.vol9iss1pp21-30