A GENETIC BASED TECHNIQUE FOR THE DETERMINATION OF POWER SYSTEM TOPOLOGICAL OBSERVABILITY

Authors

  • S. Vazquez-Rodriguez
  • R. J. Duro

DOI:

https://doi.org/10.47839/ijc.2.2.199

Keywords:

Manufacturing plant control, evolution, genetic algorithms

Abstract

In this paper we have addressed the problem of observability of power systems from the point of view of topological observability and using genetic algorithms for its determination. The objective is to find a way to determine if a system is observable by establishing if a spanning tree of the system that verifies certain properties with regards to the use of available measurements can be obtained. To this end we have developed a genotype-phenotype transformation scheme for genetic algorithms that permits using very simple genetic operators over integer based chromosomes which after a building process can become very complex trees. The procedure was successfully applied to standard benchmark systems and we present some results for one of them.

References

A. Monticelli, F. F. Wu. Observability analysis for orthogonal transformation based state estimation, IEEE Transactions on Power Systems, Vol. PWRS-1, No. 1, Febr. 1986. pp. 201-208.

F. F. Wu, W-H. E. Liu, S-M Lun. Observability analysis and bad data processing for state estimation with equality constraints, IEEE Transactions on Power Systems, Vol. 3, No. 2, May 1988. pp. 541-548.

F. F. Wu, W-H. E. Liu, L. Holten, A. Gjelsvik, S. Aam. Observability analysis and bad data processing for state estimation using Hachtel's augmented matrix method, IEEE Transactions on Power Systems, Vol. 3, No. 2, May 1988. pp. 604-611.

G. R. Krumpholz, K. A. Clements, P. W. Davis. Power system observability: a practical algorithm using network topology, IEEE Trans. Power Apparatus and Systems, Vol. PAS-99, No. 4, July/Aug. 1980. pp. 1534-1542.

K. A. Clements, G. R. Krumpholz, P. W. Davis. Power system state estimation with measurement deficiency: an algorithm that determines the maximal observable subnetwork, IEEE Trans. Power Apparatus and Systems, Vol. PAS-101, No. 9, Sept. 1982. pp. 3044-3052.

V. H. Quintana, A. Simoes-Costa, A. Mandel. Power system topological observability using a direct graph-theoretic approach, IEEE Trans. Power Apparatus and Systems, Vol. PAS-101, No. 3, March 1982. pp. 617-626.

Hiroyuki Mori, Senji Tsuzuki. A fast method for topological observability analysis using a minimum spanning tree technique, IEEE transactions on power systems, Vol.6, No.2, May 1991. pp. 491-500.

H.Mori, H.Tanaka. A genetic approach to power system topological observality. Proc. IEEE International Symp. on Circuits and Systems, N.Y. 1994, pp.1141-1144.

H. Mori. Power System Topological Observability Index and Maximization with a Genetic Algorithm. Proc. of 1993 International Symposium on Nonlinear Theory and Its Application (NOLTA'93), Dec. 1993, pp. 1069-1072.

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Published

2014-08-01

How to Cite

Vazquez-Rodriguez, S., & Duro, R. J. (2014). A GENETIC BASED TECHNIQUE FOR THE DETERMINATION OF POWER SYSTEM TOPOLOGICAL OBSERVABILITY. International Journal of Computing, 2(2), 16-21. https://doi.org/10.47839/ijc.2.2.199

Issue

2003, Volume 2, Issue 2

Section

Articles

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