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Fault Diagnosis on Electrical Distribution Systems Based on Fuzzy Logic

  • Ramón PerezEmail author
  • Esteban Inga
  • Alexander Aguila
  • Carmen Vásquez
  • Liliana Lima
  • Amelec Viloria
  • Maury-Ardila Henry
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

The occurrence of faults in distribution systems has a negative impact on society, and their effects can be reduced by fast and accurate diagnostic systems that allow to identify, locate, and correct the failures. Since the 1990s, fuzzy logic and other artificial intelligence techniques have been implemented to identify faults in distribution systems. The main objective of this paper is to perform fault diagnoses based on fuzzy logic. For conducting the study, the IEEE 34-Node Radial Test Feeder is used. The data was obtained from ATPDraw-based fault simulation on different nodes of the circuit considering three different fault resistance values of 0, 5, and 10 ohms. The fuzzy rules to identify the type of fault are defined using the magnitudes of the phase and neutral currents. All measurements are taken at the substation, and the results show that the proposed technique can perfectly identify and locate the type of failure.

Keywords

Distribution systems Fault location Fault type Fuzzy logic 

References

  1. 1.
    Thukaram, D., Khincha, H., Vijaynarasimha, H.: Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Trans. Power Delivery 20, 710–721 (2005)CrossRefGoogle Scholar
  2. 2.
    Mirzaei, M., Kadir, M., Moazami, E., Hizam, H.: Review of fault location methods for distribution power system. Aust. J. Basic Appl. Sci. 3, 2670–2676 (2009)Google Scholar
  3. 3.
    Prakash, M., Pradhan, S., Roy, S.: Soft computing techniques for fault detection in power distribution systems : a review. In: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), pp. 1–6 (2014)Google Scholar
  4. 4.
    Mustafa, M.: A novel fuzzy cause-and-effect-networks based methodology for a distribution system’s. In: 2013 3rd International Conference on Electric Power and Energy Conversion Systems (EPECS), pp. 1–6 (2013)Google Scholar
  5. 5.
    Ying, H., Ying, H., Ding, Y., Ding, Y., Li, S., Li, S., Shao, S., Shao, S.: Typical Takagi-Sugeno and Mamdani fuzzy systems as universal approximators. In: IEEE International Conference on Fuzzy Systems, pp. 824–828 (1998)Google Scholar
  6. 6.
    Mamdani, E., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7, 1–13 (1975)CrossRefGoogle Scholar
  7. 7.
    Schnitman, L., Yoneyama, T.: An efficient implementation of a learning method for Mamdani fuzzy models. In: Proceedings of the Sixth Brazilian Symposium on Neural Networks, vol. 1, pp. 38–43 (2000)Google Scholar
  8. 8.
    Distribution System Analysis Subcommittee: IEEE 34 Node Test Feeder (2000)Google Scholar
  9. 9.
    Pérez, R., Vásquez, C.: Fault location in distribution systems with distributed generation using support vector machines and smart meters. In: IEEE Ecuador Technical Chapters Meeting (ETCM), pp. 1–6 (2016)Google Scholar
  10. 10.
    Das, B.: Fuzzy logic-based fault-type identification in unbalanced radial power distribution system. IEEE Trans. Power Delivery 21, 278–285 (2006)CrossRefGoogle Scholar
  11. 11.
    Mahanty, R., Gupta, P.: A fuzzy logic based fault classification approach using current samples only. Electr. Power Syst. Res. 77, 501–507 (2007)CrossRefGoogle Scholar
  12. 12.
    Babayomi, O., Oluseyi, P., Keku, G., Ofodile, N.: Neuro-fuzzy based fault detection identification and location in a distribution network. In: Proceedings of 2017 IEEE PES-IAS PowerAfrica Conference: Harnessing Energy, Information and Communications Technology (ICT) for Affordable Electrification of Africa, PowerAfrica, pp. 164–168 (2017)Google Scholar
  13. 13.
    Ri, H.: A hybrid wavelet singular entropy and fuzzy system based fault detection and classification on distribution line with distributed generation. In: 2nd IEEE International Conference on Recent Trends in Electronics Information and Communication Technology (RTEICT), pp. 1473–1477 (2017)Google Scholar
  14. 14.
    Izquierdo, N.V., Viloria, A., Gaitán-Angulo, M., Bonerg, O., Lezama, P., Erase, J.J.C., Gutiérrez, A.S.: Methodology of application of diffuse mathematics to performance evaluation. Int. J. Control Theory Appl. (2016). ISSN 0974-5572Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ramón Perez
    • 1
    Email author
  • Esteban Inga
    • 1
  • Alexander Aguila
    • 1
  • Carmen Vásquez
    • 2
  • Liliana Lima
    • 3
  • Amelec Viloria
    • 4
  • Maury-Ardila Henry
    • 4
  1. 1.Electronic DepartmentUniversidad Politécnica SalesianaQuitoEcuador
  2. 2.Electrical DepartmentUniversidad Nacional Experimental Politécnica “Antonio José de Sucre”BarquisimetoVenezuela
  3. 3.Basic Sciences Department, Mathematical SectionUniversidad Nacional Experimental Politécnica “Antonio José de Sucre”BarquisimetoVenezuela
  4. 4.Universidad de la CostaBarranquillaColombia

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