Advertisement

A Strategy for Evolutionary Spanning Tree Construction within Constrained Graphs with Application to Electrical Networks

  • Santiago Vazquez-Rodriguez
  • Richard J. Duro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

In this work we present a particular encoding and fitness evaluation strategy for a genetic approach in the context of searching in graphs. In particular, we search for a spanning tree in the universe of directed graphs under certain constraints related to the topology of the graphs considered. The algorithm was also implemented and tested as a new topological approach to electrical power network observability analysis and was revealed as a valid technique to manage observability analysis when the system is unobservable. The algorithm was tested on benchmark systems as well as on networks of realistic dimensions.

Keywords

Power System Encode Scheme Observability Analysis Sampling Window Topological Approach 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schweppe, F.C., Wildes, J.: Power system static-state estimation, part i: exact model. IEEE Transactions on Power Apparatus and Systems PAS-89(1), 120–125 (1970)CrossRefGoogle Scholar
  2. 2.
    Schweppe, F.C., Rom, D.B.: Power system static-state estimation, part ii: approximate model. IEEE Transactions on Power Apparatus and Systems PAS-89(1), 125–130 (1970)CrossRefGoogle Scholar
  3. 3.
    Schweppe, F.C.: Power system static-state estimation, part iii: implementation. IEEE Transactions on Power Apparatus and Systems PAS-89(1), 130–135 (1970)CrossRefGoogle Scholar
  4. 4.
    Monticelli, A., Wu, F.F.: Network observability identification of observable islands and measurement placement. IEEE Transactions on Power Apparatus and Systems PAS-104(5), 1035–1041 (1985)CrossRefGoogle Scholar
  5. 5.
    Monticelli, A., Wu, F.F.: Network observability theory. IEEE Transactions on Power Apparatus and Systems PAS-104(5), 1042–4048 (1985)CrossRefGoogle Scholar
  6. 6.
    Krumpholz, G., Clements, K., Davis, P.: Power system observability: a practical algorithm using network topology. IEEE Transactions on Power Apparatus and Systems PAS-99(4), 1534–1542 (1980)CrossRefGoogle Scholar
  7. 7.
    Clements, K., Krumpholz, G., Davis, P.: Power system state estimation with measurement deficiency: an algorithm that determines the maximal observable subnetwork. IEEE Transactions on Power Apparatus and Systems PAS-101(9), 3044–3052 (1982)CrossRefGoogle Scholar
  8. 8.
    Nucera, R.R., Gilles, M.L.: Observability analysis a new topological algorithm. IEEE Transactions on Power Systems 6(2), 466–473 (1991)CrossRefGoogle Scholar
  9. 9.
    Mori, H., Tanaka, H.: A genetic approach to power system topological observability. In: IEEE Proceedings of International Symposium on Circuits and Systems 1991, ISCAS 1991, vol. 2, pp. 1141–1144 (1991)Google Scholar
  10. 10.
    Mori, H.: A ga-based method for optimizing topological observability index in electric power networks. In: IEEE Proceedings of the First Conference on Evolutionary Computation 1994. IEEE World Congress on Computational Intelligence, vol. 2, pp. 565–568 (1994)Google Scholar
  11. 11.
    Vazquez-Rodriguez, S., Duro, R.: A genetic baseed technique for the determination of power system topological observability. International Scientific Journal of Computing 2(2)Google Scholar
  12. 12.
    Vazquez-Rodriguez, S., Faiña, A., Neira-Dueñas, B.: An evolutionary technique with fast convergence for power system topological observability analysis. In: Proceedings IEEE World Congress on Computational Intelligence, WCCI 2006, pp. 3086–3090 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Santiago Vazquez-Rodriguez
    • 1
  • Richard J. Duro
    • 1
  1. 1.Grupo Integrado de IngenieríaUniversidad de La CoruñaFerrolSpain

Personalised recommendations