Advertisement

A Fuzzy-Based Failure Modes and Effects Analysis (FMEA) in Smart Grids

  • Andrés A. ZúñigaEmail author
  • João F. P. Fernandes
  • P. J. Costa Branco
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 918)

Abstract

A Smart Grid combines Information and Communication Technology (ICT’s) with innovative power equipment in a fully autonomous and sustainable electrical grid. However, their growing and complex interdependence must be considered in a Smart Grid reliability analysis. Reliable data about its components is not available or even inaccurate most of the time to apply in a Smart Grid the classic method of Failure Modes and Effect Analysis (FMEA). For this reason, this work proposes to a fuzzy-based FMEA that is designed to compute the failure modes risk level in Smart Grids. The results achieved show its capability in improving not only the perception of risk, but also rank in a better way the impact of failure modes.

Keywords

FMEA Fuzzy systems Smart Grid Risk analysis 

Notes

Acknowledgements

This work has been partially supported by: Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT) of the Ecuadorian Government, and also supported by national funds through the Fundação para a Ciência e a Tecnologia (FCT) of the Portuguese Government with references UID/EEA/50008/2013 and through IDMEC, under LAETA, project UID/EMS/50022/2013.

References

  1. 1.
    Teixeira, Â.P.: FMEA/FMECA. Class Notes - Systems Reliability and Maintainability. Instituto Superior Técnico, Universidade de Lisboa, Lisboa (2017)Google Scholar
  2. 2.
    Liu, H.-C.: FMEA Using Uncertainty Theories and MCDM Methods. Springer, Singapore (2016)Google Scholar
  3. 3.
    International Electrotechnical Commission: IEC 60812:2006 – Procedure for Failure Mode and Effects Analysis (FMEA), Geneva, Switzerland (2006)Google Scholar
  4. 4.
    Dinmohammadi, F., Shafiee, M.: A fuzzy-FMEA risk assessment approach for offshore wind turbines. Int. J. Prognost. Health Manage. 4(Sp2), 1–10 (2013)Google Scholar
  5. 5.
    Villarini, M., Cesarotti, V., Alfonsi, L., Introna, V.: Optimization of photovoltaic maintenance plan by means of a FMEA approach based on real data. Energy Convers. Manage. 152, 1–12 (2017)CrossRefGoogle Scholar
  6. 6.
    Pourramazan, A., Saffari, S., Barghandan, A.: Study of Failure Mode and Effect Analysis (FMEA) on capacitor bank used in distribution power systems. Int. J. Innov. Res. Elect. Electron. Instr. Control Eng. 5(2), 113–118 (2007)Google Scholar
  7. 7.
    Araújo, W.: Metodologia fmea-fuzzy aplicada à gestão de indicadores de continuidade individuais de sistemas de distribuição de energia elétrica. Master Thesis, Universidade Federal de Santa Catarina, Florianópolis S.C., Brasil (2008)Google Scholar
  8. 8.
    Kaur, J., Singh, B.N.: Condition monitoring of power transformer using failure modes and effects analysis (FMEA). Int. J. Innov. Res. Sci. Eng. Technol. 6(9), 19108–19115 (2017)Google Scholar
  9. 9.
    Yssaad, B., Khiat, M., Chaker, A.: Maintenance optimization for equipment of power distribution system based on FMECA method. Acta Elecht. 53(3), 218–223 (2012)Google Scholar
  10. 10.
    Baleia, A.: Failure modes and effects analysis (FMEA) for smart electrical distribution systems. Master Thesis, University of Lisbon, Lisbon (2018)Google Scholar
  11. 11.
    Vinodh, S., Aravindraj, S., Narayanan, R.S., Yogeshwaran, N.: Fuzzy assessment of FMEA for rotary switch: case study. TQM J. 24(5), 461–475 (2012)CrossRefGoogle Scholar
  12. 12.
    Bowles, J.B., Pelaez, C.E.: Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliab. Eng. Syst. Saf. 50(2), 203–213 (1995)CrossRefGoogle Scholar
  13. 13.
    Tay, K.M., Lim, C.P.: Fuzzy FMEA with a guided rules reduction system for prioritization of failures. Int. J. Qual. Reliab. Manage. 23(8), 1047–1066 (2006)Google Scholar
  14. 14.
    Jang, J.-S.R., Sun, T.-S.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Englewood Cliffs (1997)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Andrés A. Zúñiga
    • 1
    Email author
  • João F. P. Fernandes
    • 1
  • P. J. Costa Branco
    • 1
  1. 1.IDMEC, LAETA, Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal

Personalised recommendations