International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Power System Voltage Stability Classification Using Interior Point Method Based Support Vector Machine(IPMSVM)

  • Song, Hwa-Chang (Department of Electrical Engineering, Seoul Nat'l University of Technology) ;
  • Dosano, Rodel D. (School of Electronic and Information Eng., Kunsan Nat'l University) ;
  • Lee, Byong-Jun (School of Electrical Engineering, Korea University)
  • Received : 2009.03.17
  • Accepted : 2009.06.15
  • Published : 2009.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper present same thodology for the classification of power system voltage stability, the trajectory of which to instability is monotonic, using an interior point method based support vector machine(IPMSVM). The SVM based voltage stability classifier canp rovide real-time stability identification only using the local measurement data, without the topological information conventionally used.

Keywords

References

  1. R. O. Burnett Jr.and M. M. Butts, "Power system applications for phasor measurement units, " IEEE Computer Applications in Power, vol.7,no.1,pp.8–13, 1994
  2. C. Rehtanz and J. Bertsch, "A new wide area protec- tion system," Proc.of 2001 IEEE PowerTech Conference, vol.4, 2001
  3. C. W. Taylor, D. C. Erickson, K. E. Martin, R. E. Wil- son, and V. Venkatasubramanian, "WACS-Wide area stability voltage control system: R and D and online demonstration," Proc. of IEEE, vol.93, no.5, pp.892-906, 2005 https://doi.org/10.1109/JPROC.2005.846338
  4. V. Vapnik, Statistical learning theory, John Wiley& Sons, NewYork, 1998
  5. N. Cristianini and J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, New York, 2000
  6. C. J. C. Burges, A tutorial on support vector ma- chines for pattern recognition, Online:www.umiacs. umd.edu/users/joseph/support-vector-machines4.pdf
  7. S. Mebrotra, "On the implementation of aprimal-dual interior point method," SIAM Journal on Optimization, vol.2, no.4, pp.575–601, 1992
  8. B. -M. Yang, C. -K. Kim, G. -J. Jung, and Y. -H. Moon, "Verification of hybrid real time HVDC simulator in Cheju-Haenam HVDC system," Journal of Electrical Engineering & Technology, vol.1, no.1, pp.23–27, 2006
  9. J. H. Wright, Primal-dual interior-point methods, SIAM, Philadelphia, 1997
  10. P. Kundur, Power system stability and control, McGraw-Hill, NewYork, 1994
  11. C. Taylor, Power system voltage stability, McGraw-Hill, NewYork, 1994
  12. T. Van Cutsem and C. Vournas, Voltage stability of electric power systems, Kluwer Academic Publisher, Boston,1998
  13. D. Michie, D. J. Spiegelhalter and C. C. Taylor(eds.), Machine learning, neural and statistical classification, Online:http://www.amsta.leeds.ac.uk/charles/statlog/whole.pdf
  14. Powertech Labs, TSAT user manual, Powertech Labs Inc., 2007

Cited by

  1. A Short-Term Wind Speed Forecasting Through Support Vector Regression Regularized by Particle Swarm Optimization vol.11, pp.4, 2011, https://doi.org/10.5391/IJFIS.2011.11.4.247
  2. Fuzzy-Enforced Complementarity Constraints in Nonlinear Interior Point Method-Based Optimization vol.13, pp.3, 2013, https://doi.org/10.5391/IJFIS.2013.13.3.171