The Growing Hierarchical Self-Organizing Feature Maps And Genetic Algorithms for Large Scale Power System Security

  • M. Boudour
  • A. Hellal
Conference paper


This paper proposes a new methodology which combines supervised learning, unsupervised learning and genetic algorithm for evaluating power system dynamic security. Based on the concept of stability margin, pre-fault power system conditions are assigned to the output neurons on the two-dimensional grid with the growing hierarchical self-organizing map technique (GHSOM) via supervised ANNs which perform an estimation of post-fault power system state. The technique estimates the dynamic stability index that corresponds to the most critical value of synchronizing and damping torques of multimachine power systems. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping in order to provide an adaptive neural net architecture during its unsupervised training process. Numerical tests, carried out on a IEEE 9 bus power system are presented and discussed. The analysis using such method provides accurate results and improves the effectiveness of system security evaluation.


Power System Output Neuron Security Assessment Dynamic Security False Dismissal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Anderson, P.M, Fouad, A.A.(1977) Power system control and stability, The Iowa State University PressGoogle Scholar
  2. [2]
    Yu, Y.N.(1983) Electric power system dynamics, New York Academic PressGoogle Scholar
  3. [3]
    Boudour, M., Bensenouci, A.(1994) Effect of load characteristics and PSS on the stability regions using damping and synchronizing torques. Middle East Power Conference, Assiut, EgyptGoogle Scholar
  4. [4]
    Boudour, M., Bensenouci, A.(1998) Multimachine power system stability assessment using torques indexes and modal analysis. AMSE Journal Modelling Measurement and Control 69: 55–69Google Scholar
  5. [5]
    Aggoune M.E.(1988) Power System Security Assessment Using Artificial Neural Networks, PHD Thesis, University of WashingtonGoogle Scholar
  6. [6]
    Feilat E.A.(2000) Power system dynamic stability using least squares, Kalman filtering and genetic algorithms. Proceeding of the IEEE South East Conference 9: 489–492Google Scholar
  7. [7]
    Mugwanya, D.K., Van Ness, J.E.(1987) Mode coupling in power systems. IEEE Trans. Power System 2: 264–270Google Scholar
  8. [5]
    De Oliveira, S.E.M.(1994) Power system steady state stability as affected by static var compensators. IEEE Trans. Power Systems 9: 109–119CrossRefGoogle Scholar
  9. [6]
    Yoshimura, K., Uchida, N.(2001) Proposal of remote signal input PSS for improving power transfer capability considering damping and synchronizing torques. IEEE Power Engineering Society Winter Meeting 3:1329–1334CrossRefGoogle Scholar
  10. [7]
    Pourbeik, P., Gibbard, M.J.(1994) Damping and synchronizing torques induced on generators by facts stabilizers in multimachine power systems. IEEE Trans. Power Systems 11:1920–1925CrossRefGoogle Scholar
  11. [8]
    El-Sharkawi, M.A., et al. (1989) Dynamic security assessment of power systems using artificial neural networks. Proc. of second Symposium on Expert Systems, pp. 378–384Google Scholar
  12. [9]
    Mansour, Y., et al., Dynamic security contingency screening and ranking using neural networks. IEEE Trans. Neural Networks, pp. 942–950Google Scholar
  13. [10]
    Meyer, B., Nativel, G.(1999) New trends Requirements for dynamic security assessment. Control Engineering Practice 7: 375–380CrossRefGoogle Scholar
  14. [11]
    Srinivasan, D., et al.(1998) Power system security assessment and enhancement using artificial neural network. Proceedings of the International Conference on Energy Management 2:. 582–587Google Scholar
  15. [12]
    Niebur, D., Germond, A.J.(1992) Power system security assessment using the Kohonen neural network classifier. IEEE Trans. Power Systems 7:865–872CrossRefGoogle Scholar
  16. [13]
    Jensen, C.A.(2001) Power system security assessment using neural networks: feature selection using fisher discrimination. IEEE Trans. Power Systems 16: 757–763Google Scholar
  17. [14]
    Brandwain, V. et al.(1997) Severity Indices for Contingency Screening in Dynamic Security Assessment. IEEE Trans. Power Systems (12):1136–1142CrossRefGoogle Scholar
  18. [15]
    Vafai, H., Jong, K.( 1992) Genetic algorithms as a tool for feature selection in machine learning. Proc. of fourth international Conference on tools with Artificial Intelligence, Arlinton, VA, pp. 200–203Google Scholar
  19. [16]
    Reed, R.D., Marks, R.J.(1999) Neural smithing: Supervised learning in feedforward ANN, Cambridge, MA: MIT PressGoogle Scholar
  20. [17]
    Veerasooriya, S., El-Sharkawi, M.A.(1991) Use of Karhunen-Loe’s expansion in training neural networks for static security assessment. Proc. of first international forum on Applications of Neural Netwoks to Power Systems, Seattle, WA, pp. 59–64Google Scholar
  21. [18]
    Kohonen, T.( 1999) Fast Evolutionary Learning With Batch-Type Self-Organizing Maps, Neural Process LettGoogle Scholar
  22. [19]
    Rauber, A., Merkel, D., Dittenbach, M. (2002) The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Trans. Neural Networks 13: 1331–1341CrossRefGoogle Scholar
  23. [20]
    Boudour, M., Hellal, A.(2003) Self-organizing feature maps for power system dynamic security assessment using synchronizing and damping torques technique. Proceedings of the 29th Conference of the IEEE Industrial Electronics Society, Roanoke, Virginia, pp.752–758Google Scholar
  24. [21]
    Marks II, R.J., et al.(1988) The effect of stochastic interconnects in artificial neural network classification. IEEE International Conference on Neural NetworksGoogle Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • M. Boudour
    • 1
  • A. Hellal
    • 2
  1. 1.Electrical Engineering DepartmentUniversity of Sciences & TechnologyAlgiersAlgeria
  2. 2.Electrical Engineering DepartmentPolytechnic InstituteAlgiersAlgeria

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