A novel petri nets algorithm using conditional probability for the evaluation of composite power system reliability

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Reliability of an electrical power system plays a vital role in providing continuous power supply to consumers with greater quality. The power demand is increasing day by day due to the increased population, modern society and are seeking highly reliable power. The assessment of reliability is very difficult due to the presence of large number of components and complex power system network con-figurations. This paper address a novel useful step by step algorithm for the assessment of average power availability at load buses us-ing the concept of modified Petri nets with conditional probability. The proposed algorithm is very efficient and can applicable to any number of the bus system. The proposed algorithm is tested with Roy Billiton practical example, IEEE 6 bus, IEEE 14 bus and IEEE RTS-96 bus system. The obtained results are validated by Monte Carlo simulation method, Classical Node elimination method and mod-ified minimal cut set method.

  • Keywords

    Conditional Probability; Failure Rate; Monte Carlo Simulation; Repair Rate; Series-Parallel Equivalence; Star Delta Conversion.

  • References

      [1] R. Billinton and R. N. Allan, Reliability Evaluation of Engineering Systems. New York: Plenum, 1992.

      [2] P. Fox-Penner, “Smart Power: Climate Change, the Smart Grid, and the Future of Electric Utilities,” Washington, DC: Island Press, 2005.

      [3] G. T. Heydt, “The next generation of power distribution systems,” IEEE Trans. Smart Grid, vol. 1, no. 3, pp. 225–235, 2010. https://doi.org/10.1109/TSG.2010.2080328.

      [4] I. S. Bae and J. O. Kim, “Reliability evaluation of customers in a micro grid,” IEEE Trans. Power Syst., vol. 23, pp. 1416–1422, 2008. https://doi.org/10.1109/TPWRS.2008.926710.

      [5] R. Ramakumar, Engineering Reliability Fundamentals and Applications. Englewood Cliffs, NJ: Prentice-Hall, 1993.

      [6] E. Carpaneto, A. Mosso, A. Ponta, and E. Roggero, “Comparison of reliability and availability evaluation techniques for distribution network systems,” in Proc. Annu. Reliability and Maintainability Symp. Jan. 2002, pp. 563–568.

      [7] M. Fotuhi-Firuzabad, R. Billinton, T. S. Munian, and B. Vinayagam, “A novel approach to determine minimal tie-sets of complex network,” IEEE Trans. Reliab., vol. 53, no. 1, pp. 61–70, 2004. https://doi.org/10.1109/TR.2004.824834.

      [8] J. Malinowski, “A new efficient algorithm for generating all minimal tie-sets connecting selected nodes in amesh-structured network,” IEEE Trans. Reliab., vol. 59, no. 1, pp. 203–211, Mar. 2010. https://doi.org/10.1109/TR.2009.2036712.

      [9] T. Bharath Kumar, O. Chandra Sekhar, M. Ramamoorty, S.V. N.L. Lalitha, "Evaluation of power capacity availability at loadbus in a composite power system", IEEE J. Emerg. Sel. Top.Power Electron. 4 (4) (2016) 1324–1331. https://doi.org/10.1109/JESTPE.2016.2615655.

      [10] T. Bharath Kumar, O. Chandra Sekhar, M. Ramamoorty, "Composite power system reliability evaluation using modified minimal cut set approach", Alexandria Engineering Journal - Elsevier, 2017 (Accepted- Early access). https://doi.org/10.1016/j.aej.2017.09.008.

      [11] T. Murata, “State equation, controllability, and maximal matchings ofPetri nets,” IEEE Trans. Autom. Control, vol. 22, p. 412, 1977. https://doi.org/10.1109/TAC.1977.1101509.

      [12] Y. Liu and C. Singh, “Reliability evaluation of composite power systems using Markov cut-set method,” IEEE Trans. Power Syst., vol. 25, no. 2, pp. 777–785, May 2010. https://doi.org/10.1109/TPWRS.2009.2033802.

      [13] G. S. Hura, K. B. Misra, Ed., “Use of Petri nets for system reliabilityevaluation,” in New Trends in Reliability Evaluation. Amsterdam,The Netherlands: Elsevier Science, 1992, pp. 339–368.

      [14] G. T. Heydt and T. J. Graf, “Distribution system reliability evaluation using enhanced samples in a Monte Carlo approach,” IEEE Trans. Power Syst., vol. 25, no. 4, pp. 2006–2008, Nov. 2010. https://doi.org/10.1109/TPWRS.2010.2045929.

      [15] R. Billinton, H. Chen, R. Ghajar, “Time-series models for reliability evaluation of power systems including wind energy” Microelectronics and Reliability, v. 36, 1996, pp. 1253-61. https://doi.org/10.1016/0026-2714(95)00154-9.

      [16] A. Jonnavithula, “Composite system reliability evaluation using sequential Monte Carlo simulation,” Ph. D. Thesis, University of Saskatchewan, 1997.

      [17] A. Sankarakrishnan, R. Billinton, “Sequential Monte Carlo simulation for composite power system reliability analysis with time varying loads,” IEEE Transactions on Power Systems, v. 10, no. 3, August 1995, pp. 1540-1545. https://doi.org/10.1109/59.466491.

      [18] P. Wang, R. Billinton, “Time sequential distribution system reliability worth analysis considering time varying load and cost models,” IEEE Transactions on Power Delivery, v.14, no. 3, Jul 1999, pp. 1046-1051. https://doi.org/10.1109/61.772352.

      [19] G. B. Jasmon and K. W. Foong “A method for evaluating all the minimal cut of a graph,” IEEE Trans. on reliability, Vol R-25, pp226-233, Oct 1976.

      [20] Jose R. Celaya, Alan A. Desrochers, and Robert J. Graves “Modelling and Analysis of Multi-agent Systems using Petri nets”IEEE International Conference on Systems, Man and Cybernetics, 2007.

      [21] Nancy G. Leveson AND janice L. STOLZY “Safety Analysis Using Petr Nets,”IEEE Transactions on Software Engineering, Vol SE-13, Mar 1987.




Article ID: 10909
DOI: 10.14419/ijet.v7i2.10909

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.