Skip to main content

Integral Fuzzy Power Quality Assessment for Decision Support System at Management of Power Network with Distributed Generation

  • Conference paper
  • First Online:
Information and Software Technologies (ICIST 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 920))

Included in the following conference series:

Abstract

This paper is devoted to the development of scientific and methodological foundations of improvement the information support of decision at management of power network with distributed generation. It is proposed to consider the power quality index as the main criterion of management. Using the theory of fuzzy sets, the assessment of the conformity of power quality indicators to electric energy quality limits is done. A method for estimating the quality of electrical energy is proposed which represents the measured histogram as a fuzzy representation indicator of quality of electric energy in the form of fuzzy sets with a step membership function. The method of integral evaluation of electrical energy quality for different types of load is developed. The presented method allows to formulate rules for managing the operating modes of the distributed electrical network by the decision support system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pakštas, A., Shulyma, O., Shendryk, V.: On defining and assessing of the energy balance and operational logic within hybrid renewable energy systems. Commun. Comput. Inf. Sci. 639, 151–162 (2016)

    Google Scholar 

  2. Shulyma, O., Shendryk, V., Parfenenko, Y., Shendryk, S.: The model for decision support on design of the hybrid renewable energy system. In: Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017, pp. 47–50 (2017)

    Google Scholar 

  3. EN 50160:2010 Voltage Characteristics of electricity supplied by public distribution networks

    Google Scholar 

  4. Guide to the Expression of Uncertainty in Measurement: First Edition, p. 101. ISO, Switzerland (1993)

    Google Scholar 

  5. Mauris, G., Lassere, V., Foulley, L.: A fuzzy approach for the expression of uncertainty in measurement. Measurement 29, 109–121 (2001)

    Article  Google Scholar 

  6. Mauris, G., Berrah, L., Foulloy, L., Haurat, A.: Fuzzy handling of measurement errors in instrumentation. IEEE Trans. Measur. 1(49), 43–58 (2000)

    Google Scholar 

  7. Canha, L.N., Pereira, P.R., Milbradt, R., Da Rosa Abaide, A., Schmitt, K.E.K., De Abreu Antunes, M.: Intelligent voltage regulator to distributed voltage control in smart grids. In: 52nd International Universities Power Engineering Conference, UPEC 2017, 2017 January, pp. 1–6 (2017). https://doi.org/10.1109/upec.2017.8231977

  8. Arcos-Aviles, D., Pascual, J., Marroyo, L., Sanchis, P., Guinjoan, F.: Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Trans. Smart Grid 9(2), 530–543 (2018). https://doi.org/10.1109/TSG.2016.2555245

    Article  Google Scholar 

  9. Liu, X.N., Wei, J., Ye, S.Y., Chen, B., Long, C.: Research on uncertainty evaluation measure and method of voltage sag severity. In: IOP Conference Series: Earth and Environmental Science, vol. 108, no. 5 (2018). https://doi.org/10.1088/1755-1315/108/5/052098

    Article  Google Scholar 

  10. Miroshnik, A.A., Tymchuk, S.A.: Uniform distribution of loads in the electric system 0,38/0,22 kV using genetic algorithms. Tech. Electrodynamics 4, 67–73 (2013)

    Google Scholar 

  11. Piegat, A.: Fuzzy Modeling and Control. Phisical-Verl, Heidelberg, New York (2001). 728

    Book  Google Scholar 

  12. Tymchuk, S.A., Miroshnyk, A.A.: Quality assessment of power in distribution networks 0.38/0.22 kV in the fuzzy form. In: Global Science and Innovation. Materials of the II International Scientific Conference, vol. II, Chicago, USA, pp. 288–299 (2014)

    Google Scholar 

  13. Tymchuk, S.A., Miroshnyk, A.A.: Assess electricity quality by means of fuzzy generalized index. East Eur. J. Adv. Technol. 3/4(75), 26–31 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vira Shendryk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tymchuk, S., Miroshnyk, O., Shendryk, S., Shendryk, V. (2018). Integral Fuzzy Power Quality Assessment for Decision Support System at Management of Power Network with Distributed Generation. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-319-99972-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99972-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99971-5

  • Online ISBN: 978-3-319-99972-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics