Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Vulnerability Assessment of a Large Sized Power System Using Neural Network Considering Various Feature Extraction Methods

  • Haidar, Ahmed M. A (Dept. of Electrical, Electronic and Systems Engineering, The National University of Malaysia-UKM) ;
  • Mohamed, Azah (The National University of Malaysia-UKM) ;
  • Hussian, Aini (The National University of Malaysia-UKM)
  • Published : 2008.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network(ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.

Keywords

References

  1. V. L. Paccar and M. J. Rider, "Artificial neural networks for solving the power flow problem in electric power systems", Electric Power Systems Research Elsevier, Vol. 62, pp. 139-144, June 2002 https://doi.org/10.1016/S0378-7796(02)00030-5
  2. C. A. Jensen, Mohamed A. El-Sharkawi and R. J. Marks, "Power system security assessment using neural networks: feature selection using Fisher discrimination", IEEE Transaction on Power System, Vol. 16, no 1, pp. 757-763, November 2001 https://doi.org/10.1109/59.962423
  3. H. Sawhney and B. Jeyasurya, "On-line transient stability assessment using artificial neural network", Proceedings of the IEEE on Large Engineering Systems Conference, Vol. 2, pp. 76-80, July 2004
  4. M. Delimar, I. Pavii and Z. Hebel, "Artificial neural networks in power system topology recognition", Proceedings of the IEEE EUROCON Computer as a Tool, Ljubljana, Slovenia. pp. 287-291, September 2003
  5. S. C. Tan and C. P. Lim, "Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant", IEEE Transaction on Energy Conversion, Vol. 19, no 2, pp. 369-377, June 2004 https://doi.org/10.1109/TEC.2003.821826
  6. G. R. Yousefi. "An application of artificial neural networks in power load modeling", Proceedings of the IEEE Power Engineering Society General Meeting. Iran Vol. 1, pp. 308-313, June 2005
  7. M. Kim "Application of computational intelligence to power system vulnerability assessment and adaptive protection using high speed communication", PhD Thesis, University of Washington, USA, 2002
  8. Simon P. Teeuwsen, "Oscillatory stability assessment of power system using computational intelligence" PhD Thesis. University Duishdurg-Essen, Germany 2005
  9. K. Wagsta, C. Cardie, S. Rogers and S. Schroed. "Constrained K-means Clustering with Background Knowledge", Proceedings of the Eighteenth International Conference on Machine Learning, pp. 577-584, 2001
  10. K. M. Faraoun and A. Boukelif "Neural networks learning improvement using the K-means clustering algorithm to detect network intrusions", International Journal of Computational Intelligence, Vol. 3, pp. 161-168, June 2006
  11. A. Kalousis, J. Prados and M. Hilario, "Stability of feature selection algorithms", Proceedings of the Fifth IEEE International Conference on Data Mining. pp. 8, November 2005
  12. Simon Haykin, "Neural Network: A comprehensive foundation," Prentice-Hall, 1999, pp. 156-175
  13. A. M. A. Haidar, A. Mohamed, A. Hussain. "Vulnerability assessment of a large sized power system using a new index based on power system loss," European Journal of Scientific Research. Vol. 17, no. 1, pp. 61-72, 2007
  14. M. B. Gülmezo˘glu, V. Dzhafarov, and A. Barkana, "The common vector approach and its relation to principal component analysis", IEEE Transaction on Speech and Audio Processing. Vol. 9, no. 6, pp. 655-662, September 2001 https://doi.org/10.1109/89.943343

Cited by

  1. A computational intelligence-based suite for vulnerability assessment of electrical power systems vol.18, pp.5, 2010, https://doi.org/10.1016/j.simpat.2009.12.009