Journal of Zhejiang University-SCIENCE A

, Volume 9, Issue 12, pp 1753–1764 | Cite as

Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators



We present a new approach based on honey-bee mating optimization to estimate the state variables in distribution networks including distributed generators. The proposed method considers practical models of electrical equipments such as static var compensators, voltage regulators, and under-load tap changer transformers, which have usually nonlinear and discrete characteristics. The feasibility of the proposed approach is demonstrated by comparison with the methods based on neural networks, ant colony optimization, and genetic algorithms for two test systems, a network with 34-bus radial test feeders and a realistic 80-bus 20 kV network.

Key words

Distributed generators (DGs) State estimation Honey-bee mating optimization (HBMO) 

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  1. Afshar, A., Bozog, H., Marino, M.A., Adams, B.J., 2007. Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. J. Franklin Inst., 344(5):452–462. [doi:10.1016/j.jfranklin.2006.06.001]CrossRefMATHGoogle Scholar
  2. Baran, M.E., Kelley, A.W., 1994. State estimation for real-time monitoring of distribution systems. IEEE Trans. on Power Syst., 9(3):1601–1609. [doi:10.1109/59.336098]CrossRefGoogle Scholar
  3. Baran, M.E., Freeman, L.A.A., Hanson, F., Ayers, V., 2005. Load estimation for load monitoring at distribution substations. IEEE Trans. on Power Syst., 20(1):164–170. [doi:10.1109/TPWRS.2004.840409]CrossRefGoogle Scholar
  4. Deng, Y., He, Y., Zhang, B., 2002. A branch-estimation-based state estimation method for radial distribution systems. IEEE Trans. on Power Del., 17(4):1057–1062. [doi:10.1109/TPWRD.2002.803800]CrossRefGoogle Scholar
  5. Fathian, M., Amiri, B., Maroosi, A., 2007. Application of honey bee mating optimization algorithm on clustering. Appl. Math. Comput., 190(2):1502–1513. [doi:10.1016/j. amc.2007.02.029]MathSciNetMATHGoogle Scholar
  6. Ghosh, A.K., Lubkeman, D.L., Downey, M.J., Jones, R.H., 1997. Distribution circuit state estimation using a probabilistic approach. IEEE Trans. on Power Syst., 12(1):45–51. [doi:10.1109/59.574922]CrossRefGoogle Scholar
  7. Gursoy, E., Niebur, D., 2006. On-line Estimation of Electric Power System Active Loads. Proc. IEEE Int. Joint Conf. on Neural Networks, BC, Canada, p.1689–1694. [doi:10.1109/IJCNN.2006.1716311]Google Scholar
  8. Haddad, O.B., Afshar, A., Marino, M.A., 2006. Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Res. Manag., 20(5):661–680. [doi:10.1007/s11269-005-9001-3]CrossRefGoogle Scholar
  9. Haupt, JR.L., Haupt, S.E., 1999. Practical Genetic Algorithm. John Wiely & Sons Inc.Google Scholar
  10. Kersting, W.H., 1991. Radial distribution test feeders. IEEE Trans. on Power Syst., 6(3):975–985. [doi:10.1109/59.119237]CrossRefGoogle Scholar
  11. Konjic, T., Miranda, V., Kapetanovic, I., 2005. Fuzzy inference systems applied to LV substation load estimation. IEEE Trans. on Power Syst., 20(2):742–749. [doi:10.1109/TPWRS.2005.846210]CrossRefGoogle Scholar
  12. Leou, R.C., Lu, C.N., 1996. Improving feeder voltage calculation results with telemeter data. IEEE Trans. on Power Del., 11(4):1914–1920. [doi:10.1109/61.544276]CrossRefGoogle Scholar
  13. Losi, A., Russo, M., 2005. Dispersed generation modeling for object-oriented distribution load flow. IEEE Trans. on Power Del., 20(2):1532–1540. [doi:10.1109/TPWRD.2004.838634]CrossRefGoogle Scholar
  14. Naka, S., Genji, T., Yura, T., Fukuyama, Y., 2003. A hybrid particle swarm optimization for distribution state estimation. IEEE Trans. on Power Syst., 18(1):60–68. [doi:10.1109/TPWRS.2002.807051]CrossRefGoogle Scholar
  15. Niknam, T., 2005. Impact of Distributed Generators on Volt/Var Control in Distribution Networks. PhD Thesis, Electrical Engineering Department, Sharif University of Technology.Google Scholar
  16. Niknam, T., Ranjbar, A.M., Shirani, A.R., 2005a. A new approach for distribution state estimation based on ant colony algorithm with regard to distributed generation. J. Intell. Fuzzy Syst., 16(2):119–131.Google Scholar
  17. Niknam, T., Ranjbar, A.M., Shirani, A.R., 2005b. A new approach based on ant algorithm for Volt/Var control in distribution network considering distributed generation. Iran. J. Sci. Tech., Trans. B, 29(B4):1–15.Google Scholar
  18. Roytelman, I., Shahidehpour, S.M., 1993. State estimation for electric power distribution systems in quasi real-time conditions. IEEE Trans. Power Del., 8(4):2009–2015. [doi:10.1109/61.248315]CrossRefGoogle Scholar
  19. Sakis Meliopoulos, A.P., Zhang, F., 1996. Multiphase power flow and state estimation for power distribution systems. IEEE Trans. on Power Syst., 11(2):939–946. [doi:10.1109/59.496178]CrossRefGoogle Scholar
  20. Wan, J., Miu, K.N., 2002. Zonal Load Estimation Studies in Radial Power Distribution Networks. IEEE Int. Symp. on Circuits and Systems, 5:697–700. [doi:10.1109/ISCAS.2002.1010799]Google Scholar
  21. Wang, H.B., Schulz, N.N., 2004. A revised branch current-based distribution system state estimation algorithm and meter placement impact. IEEE Trans. on Power Syst., 19(1):207–213. [doi:10.1109/TPWRS.2003.821426]CrossRefGoogle Scholar

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© Zhejiang University and Springer-Verlag GmbH 2008

Authors and Affiliations

  1. 1.Electronic and Electrical DepartmentShiraz University of TechnologyShirazIran

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