Algorithms for Selecting and Interconnecting Switches to Automate Power Grids Considering Continuity Indexes and Reliability

  • Rafael R. C. Vaz
  • Ricardo A. P. FrancoEmail author
  • Henrique P. Corrêa
  • Flávio H. T. Vieira
  • Sérgio G. Araújo


In this paper, a method is presented for the deployment of switch clusters to automate distribution grids. The proposed methodology consists in choosing feeders of a self-healing system based on their performances in relation to the System Average Interruption Duration Index, the number of consumers, the compensation and to the probabilities of the current to exceed the pickup limit. To this end, it is proposed to consider statistical analysis of current values using decision theory and binary linear programming to determine the priorities of switch clusters. In addition, the problem of allocating communication links for commanding automated breakers in a distribution power grid is also addressed. The application of a multi-objective genetic algorithm is considered for optimizing link choice where costs and network reliability are objective functions. A novel heuristic is proposed for attributing reliability values to the links in terms of the subjacent power grid, inducing optimization convergence toward network topologies in which breakers at areas with higher fault indexes receive more communication resources.


Self-healing Continuity indexes Network reliability Switch positioning Automate power grid 



This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and CELG-D.


  1. Abdi, S., Afshar, K., Ahmadi, S., Bigdeli, N., & Abdi, M. (2014). Optimal recloser and autosectionalizer allocation in distribution networks using ipsomonte carlo approach. International Journal of Electrical Power & Energy Systems, 55, 602–611.CrossRefGoogle Scholar
  2. Abramovich, F., & Angelini, C. (2006). Bayesian maximum a posteriori multiple testing procedure. Sankhy: The Indian Journal of Statistics (2003–2007), 68(3), 436–460.MathSciNetzbMATHGoogle Scholar
  3. Armendariz, M., Gonzalez, R., Korman, M. & Nordstrm, L. (2017). Method for reliability analysis of distribution grid communications using prms-monte carlo methods. In 2017 IEEE power energy society general meeting, (pp. 1–5).Google Scholar
  4. Berger, J. O. (1985). Statistical decision theory and Bayesian analysis (Vol. 2). New York: Springer.CrossRefGoogle Scholar
  5. Bernardon, D. P., Sperandio, M., Garcia, V. J., Canha, L. N., d. R. Abaide, A., & Daza, E. F. B. (2011). Ahp decision-making algorithm to allocate remotely controlled switches in distribution networks. IEEE Transactions on Power Delivery, 26(3), 1884–1892.CrossRefGoogle Scholar
  6. Bouillet, E. (2007). Path routing in mesh optical networks (Vol. 1). Chichester: Wiley.CrossRefGoogle Scholar
  7. Cao, X., Wang, H., Liu, Y., Azizipanah-Abarghooee, R., & Terzija, V. (2017). Coordinating self-healing control of bulk power transmission system based on a hierarchical top-down strategy. International Journal of Electrical Power & Energy Systems, 90, 147–157.CrossRefGoogle Scholar
  8. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.CrossRefGoogle Scholar
  9. Ding, T., Bo, R., Li, F., & Sun, H. (2015). A bi-level branch and bound method for economic dispatch with disjoint prohibited zones considering network losses. IEEE Transactions on Power Systems, 30(6), 2841–2855.CrossRefGoogle Scholar
  10. El-hawary, M. E. (2014). The smart grid-state-of-the-art and future trends. Electric Power Components and Systems, 42(3–4), 239–250.CrossRefGoogle Scholar
  11. EPRI, E. P. R. I. (2017). Reference guide the open distribution system simulator (OpenDSS).
  12. Galo, J. J., Macedo, M. N., Almeida, L. A., & Lima, A. C. (2014). Criteria for smart grid deployment in brazil by applying the delphi method. Energy, 70, 605–611.CrossRefGoogle Scholar
  13. Garcia, P. A. N., Pereira, J. L. R., Carneiro, S., da Costa, V. M., & Martins, N. (2000). Three-phase power flow calculations using the current injection method. IEEE Transactions on Power Systems, 15(2), 508–514.CrossRefGoogle Scholar
  14. Georgilakis, P. S., & Hatziargyriou, N. D. (2013). Optimal distributed generation placement in power distribution networks: Models, methods, and future research. IEEE Transactions on Power Systems, 28(3), 3420–3428.CrossRefGoogle Scholar
  15. Grigoras, G., Cartina, G., & Bobric, E. C. (2010). Strategies for power/energy saving in distribution networks. Advances in Electrical and Computer Engineering, 10(2), 61–64.CrossRefGoogle Scholar
  16. Gupta, N., Swarnkar, A., Niazi, K. R., & Bansal, R. C. (2010). Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework. IET Generation, Transmission Distribution, 4(12), 1288–1298.CrossRefGoogle Scholar
  17. Hui, K. (2007). Monte carlo network reliability ranking estimation. IEEE Transactions on Reliability, 56(1), 50–57.CrossRefGoogle Scholar
  18. Kondo, D.V., Almeida, C.F.M., Kagan, H., Cunha, A.P., Gouvea, M.R., Felber, L.A., Braga, M.F. & Nascimento, J.A.O. (2013). A methodology for reclosers allocation in distribution networks. In 2013 IEEE PES conference on innovative smart grid technologies (ISGT Latin America), (pp. 1–8).Google Scholar
  19. MathWorks (2017). Mixed-integer linear programming algorithms.
  20. Michalewicz, Z., & Fogel, D. (2004). How to solve it: Modern heuristics (Vol. 1). Berlin: Springer.CrossRefGoogle Scholar
  21. Moradi, A., & Fotuhi-Firuzabad, M. (2008). Optimal switch placement in distribution systems using trinary particle swarm optimization algorithm. IEEE Transactions on Power Delivery, 23(1), 271–279.CrossRefGoogle Scholar
  22. Papoulis, A. (1991). Probability, random variables and stochastic processes (Vol. 3). New York: McGraw-Hill Inc.zbMATHGoogle Scholar
  23. Pombo, A. V., Murta-Pina, J. & Pires, V. F. (2017). Distributed energy resources network connection considering reliability optimization using a nsga-ii algorithm. In 2017 11th IEEE international conference on compatibility, power electronics and power engineering (CPE-POWERENG), (pp. 28–33).Google Scholar
  24. Powell, L. (2004). Power system load flow analysis (Vol. 1). New York: McGraw-Hill Education.Google Scholar
  25. Samaniego, F. J. (2007). System signatures and their applications in engineering reliability (Vol. 1). New York: Springer.CrossRefGoogle Scholar
  26. Sardou, I. G., Banejad, M., Hooshmand, R., & Dastfan, A. (2012). Modified shuffled frog leaping algorithm for optimal switch placement in distribution automation system using a multi-objective fuzzy approach. IET Generation, Transmission Distribution, 6(6), 493–502.CrossRefGoogle Scholar
  27. Shuvro, R. A., Wang, Z., Das, P., Naeini, M. R. & Hayat, M. M. (2017). Modeling impact of communication network failures on power grid reliability. In 2017 North American power symposium (NAPS), (pp. 1–6).Google Scholar
  28. Tippachon, W., & Rerkpreedapong, D. (2009). Multiobjective optimal placement of switches and protective devices in electric power distribution systems using ant colony optimization. Electric Power Systems Research, 79(7), 1171–1178.CrossRefGoogle Scholar
  29. Wang, C., & Zhang, Y. (2015). Fault correspondence analysis in complex electric power systems. Advances in Electrical and Computer Engineering, 15(1), 11–16.CrossRefGoogle Scholar
  30. Yang, J., Zhou, C., Sun, J., Xu, J. & Qi, J. (2012). The nsga-ii based computation for the multi-objective reconfiguration problem considering the power supply reliability. In 2012 China international conference on electricity distribution, (pp. 1–4).Google Scholar
  31. Zidar, M., Georgilakis, P. S., Hatziargyriou, N. D., Capuder, T., & krlec, D. (2016). Review of energy storage allocation in power distribution networks: Applications, methods and future research. IET Generation, Transmission Distribution, 10(3), 645–652.CrossRefGoogle Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  1. 1.Escola de Engenharia Elétrica, Mecânica e de ComputaçãoUniversidade Federal de Goiás (UFG)GoiâniaBrazil

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