—Security assessment is a major concern in real time operation of electric power systems. Traditional method of security assessment performed by continuous load flow analysis involves long computer time and generates voluminous results. This paper presents a practical and feasible Support Vector Machine Based Pattern Classification
(SVMBPC) approach for static security assessment in power systems. The proposed approach classifies the security status of any given operating condition in one of the four classes - Secure, Critically Secure, Insecure and Highly Insecure based on the computation of a numeric value called security index. The feature selection stage uses a simple and straightforward forward sequential method to select the best feature set from a large set of variables. The static security classifier is designed by a multi-class SVM with different parameter tuning methods. The proposed approach is implemented in New England 39 bus and IEEE 118 bus systems and the results are validated.
—Parameter selection, pattern classifier, static security, support vector machine.
S. Kalyani is with the Department of Electrical and Electronics Engineering, Kamaraj College of Engineering & Technology, Virudhunagar - 626001, Tamilnadu, India (e-mail: kal_yani_79@ yahoo.co.in).
K. S. Swarup is with the Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai - 600036, Tamilnadu, India (e-mail: firstname.lastname@example.org).
Cite:S. Kalyani and K. S. Swarup, "Static Security Assessment in Power Systems Using Multi-Class SVM with Parameter Selection Methods," International Journal of Computer Theory and Engineering vol. 5, no. 3, pp. 465-471, 2013.