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IJMLC 2011 Vol.1(2): 193-198 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.28

Static Security Evaluation in Power Systems using Multi-Class SVM with Different Parameter Selection Methods

S. Kalyani and K. S. Swarup

Abstract—Security evaluation is a major concern in real time operation of electric power systems. Traditional method of security evaluation 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 evaluation 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.

Index Terms—Parameter Selection, Pattern Classifier, Static Security, Support Vector Machine

S. Kalyani was with Indian Institute of Technology Madras. She is now with Department of EEE, K.L.N. College of Engineering, Pottapalayam – 630611, Sivagangai District, Tamilnadu, India. (Phone: +91-09443726963; Fax: +91-452-2090070; e-mail: kal_yani_79@yahoo.co.in). K.S. Swarup is with Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai – 600036, Tamilnadu, India (e-mail: swarup@ee.iitm.ac.in).


Cite: S. Kalyani, Member, IEEE and K. S. Swarup, Senior Member, IEEE, "Static Security Evaluation in Power Systems using Multi-Class SVM with Different Parameter Selection Methods," International Journal of Machine Learning and Computing vol. 1, no. 2, pp. 193-198 , 2011.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijmlc@ejournal.net

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