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Load Areas-Sorting Methodology to Aid Maintenance on Power Distribution Networks

  • Flavio TrojanEmail author
  • Danielle Costa Morais
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 280)

Abstract

To perform maintenance, the prioritization of sectors on power distribution networks still has been a challenge for managers and maintenance engineers. It should consider relevant aspects of the sectors, such as population density, the number of hospitals, and the number of schools, among others. In order to guarantee facilities reliability, this paper presents a proposal for this problematic by developing a Load Areas-sorting methodology to improve maintenance decisions on power distribution networks. It uses multi-criteria approach; characterizing criteria and weights. The initial phase of the methodology was concerned with defining of relevant operational criteria, suggested by the literature, as well as admitting new criteria that the decision makers may be deemed necessary in each scenario. The main objective was the obtaining comparison parameters to determine critical Load Areas by the occurrence of failures and allocate these areas in priority classes. An application was performed by collecting data from an electric power company in Brazil. For this application, the classes suggested were: High, Medium, and Low priority. Thus, each Load Area was allocated in a priority status regarding its importance to the company and community. The multi-criteria method used in this phase was ELECTRE TRI. With this development, it was possible to know the most critical area equalizing the decision maker’s view and operational indicators. Thus, the maintenance developments can be updated with this methodology, providing sustainability in electricity distribution by adjusted maintenance actions.

Keywords

Power distribution networks Load Areas-sorting ELECTRE TRI method 

Notes

Acknowledgements

This work is part of a research program sponsored by Coordination of Improvement of Higher Level Personnel (CAPES) and Brazilian Research Council (CNPq).

References

  1. 1.
    Bernardon, D.P., Garcia, V.J., Ferreira, A.S.Q., Canha, L.N.: Electric distribution network reconfiguration based on a fuzzy multi-criteria decision making algorithm. Electr. Power Syst. Res. 79, 1400–1407 (2009)CrossRefGoogle Scholar
  2. 2.
    Casolino, G.M., Losi, A.: Load area model accuracy in distribution systems. Electr. Power Syst. Res. 143, 321–328 (2017)CrossRefGoogle Scholar
  3. 3.
    Hu, F., Sun, K., Del Rosso, A., Farantatos, E., Bhatt, N.: Measurement-based real-time voltage stability monitoring for load areas. IEEE Trans. Power Syst. 99, 1–12 (2015)Google Scholar
  4. 4.
    Schmidt, H.P., Guaraldo, J., Lopes, M.M., Jardini, J.: Interchange able balanced and unbalanced network models for integrated analysis of transmission and distribution systems. IEEE Trans. Power Syst. 30(5), 2747–2754 (2015)CrossRefGoogle Scholar
  5. 5.
    Belhomme, R., Cerero, R., Valtorta, G., Paice, A., Bouffard, F., Rooth, R., Losi, A.: Active demand for the smart grids of the future. In: IET-CIRED Seminar on Smart Grids for Distribution (2008)Google Scholar
  6. 6.
    Poudineh, R., Jamasb, T.: Electricity supply interruptions: sectoral interdependencies and the cost of energy not served for the Scottish economy. Energy J. 38(1), 51–76 (2017)CrossRefGoogle Scholar
  7. 7.
    Costa, G.A.N., Sant’Anna, A.P.: Analysis of the efficiency of the distribution concessionaires of the Brazilian electricity system. Pesquisa em Engenharia de Produção 9(1) (2009)Google Scholar
  8. 8.
    IEEE Std 1366: IEEE Guide for Electric Power Distribution Reliability Indices. IEEE—Institute of Electrical and Electronics Engineers, vol. 1, pp. 1–92 (2012)Google Scholar
  9. 9.
    Mendes, L.F.R., Junior, M.E., Hosken, L.A.L.: Selection of electricity supply system for rural coastal properties located in the North of the State of Rio de Janeiro. Electron. Mag. Prod. Eng. 4(1), 338–345 (2011)Google Scholar
  10. 10.
    Trojan, F., Morais, D.C.: Maintenance management decision model for reduction of losses in water distribution networks. Water Resour. Manag. 29, 3459–3479 (2015)CrossRefGoogle Scholar
  11. 11.
    Gurgel, A.M., Mota, C.M.M.: A multicriteria prioritization model to support public safety planning. Pesquisa Operacional 33(2), 251–267 (2013)CrossRefGoogle Scholar
  12. 12.
    Zanazzi, J.L., Gomes, L.F.A.M., Dimitroff, M.: Group decision making applied to preventive maintenance systems. Pesquisa Operacional 34(1), 91–105 (2014)CrossRefGoogle Scholar
  13. 13.
    Mousseau, V., Figueira, J., Naux, J.P.: Using assignment examples to infer weights for ELECTRE TRI method: some experimental results. Eur J Oper. Res. 130(2), 263–275 (2001)CrossRefGoogle Scholar
  14. 14.
    ANEEL—National Electric Energy Agency—Brazil. Procedures for the Distribution of Electric Energy in the National Electric System—PRODISTGoogle Scholar
  15. 15.
    Almeida, A.T.: Decision making process in organizations. Building multicriteria decision models, 1st edn. Atlas, São Paulo (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidade Tecnológica Federal do Parana (UTFPR)Ponta GrossaBrazil
  2. 2.Universidade Federal de Pernambuco (UFPE)RecifeBrazil

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