Skip to main content

Winding Deformation Detection of Transformer Based on Sweep Frequency Impedance

  • Conference paper
  • First Online:
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 (AISI 2019)

Abstract

To improve the timeliness and accuracy of transformer winding deformation detection, the deformation of the transformer winding is studied by sweeping frequency impedance method. Based on the principle of scanning impedance method, an experimental test circuit is constructed to perform on-site detection on a 10 kV transformer. The results of transformer sweep impedance curve show that the simulated deformation fault has little effect on the low-frequency band of the sweep impedance curve, but the impedance amplitude shifts upward in the high-frequency band. At 50 Hz, the phase relation value of impedance changes significantly, which can be used as the basis for determining the winding deformation fault. It is proved that the sweep impedance method can be used well for the detection of transformer displacement faults with high sensitivity.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Bengtsson, C.: Status and trends in transformer monitoring. IEEE Trans. Power Delivery 11(3), 1379–1384 (1996)

    Article  Google Scholar 

  2. Wang, M., Vandermaar, A., Srivastava, K.: Review of condition assessment of power transformers in service. IEEE Electr. Insul. Mag. 18(6), 12–25 (2002)

    Article  Google Scholar 

  3. Abu-Siada, A., Hashemnia, N., Islam, S.: Understanding power transformer frequency response analysis signatures. IEEE Electr. Insul. Mag. 29(3), 48–56 (2013)

    Article  Google Scholar 

  4. Ryder, S.: Diagnosing transformer faults using frequency response analysis. IEEE Electr. Insul. Mag. 19(2), 16–22 (2003)

    Article  Google Scholar 

  5. Rahimpour, E., Christian, J., Feser, K.: Transfer function method to diagnose axial displacement and radial deformation of transformer windings. IEEE Trans. Power Delivery 18(2), 493–505 (2003)

    Article  Google Scholar 

  6. Christian, J., Feser, K.: Procedures for detecting winding displacements in power transformers by the transfer function method. IEEE Trans. Power Delivery 19(1), 214–220 (2004)

    Article  Google Scholar 

  7. Bagheri, M., Phung, B., Blackburn, T.: Transformer frequency response analysis: mathematical and practical approach to interpret mid-frequency oscillations. IEEE Trans. Dielectr. Electr. Insul. 20(6), 62–70 (2013)

    Article  Google Scholar 

  8. Mohammad, H., Stefan, T.: Effect of different connection schemes, terminating resistors and measurement impedances on the sensitivity of the FRA method. IEEE Dielectr. Electr. Insul. 26, 1713–1720 (2017)

    Google Scholar 

  9. Ludwikowski, K., Siodla, K., Ziomek, W.: Investigation of transformer model winding deformation using sweep frequency response analysis. IEEE Trans. Dielectr. Electr. Insul. 19(6), 1957–1961 (2012)

    Article  Google Scholar 

  10. Thirupathi, N., Srinivas, P., Sudha, K.: Performance of M/M/1 and M/D/1 queuing models on data centers with cloud computing technology using MATLAB. Int. J. Grid Distrib. Comput. 11(3), 11–22 (2018)

    Article  Google Scholar 

  11. Al-Sharrah, M., Alkandari, M.: Towards a more secured solution in RDP: on-demand desktop local admin rights. Int. J. Secur. Appl. 12(2), 45–58 (2018)

    Google Scholar 

  12. Ahuja, M., Singh, J.: Finding communities in social networks with node attribute and graph structure using jaya optimization algorithm. Int. J. Future Gener. Commun. Netw. 11(2), 33–48 (2018)

    Article  Google Scholar 

  13. Bachu, S., Manjunathachari, K.: New approach for image segmentation based on graph cuts. Int. J. Signal Process. Image Process. Pattern Recogn. 10(1), 119–130 (2017)

    Google Scholar 

  14. Han, W., Li, S., Jia, H.: Research on software trustworthiness evaluation for web application based on software product. Int. J. u - e - Serv. Sci. Technol. 10(1), 89–104 (2017)

    Article  Google Scholar 

  15. Karchi, R., Munusamy, N.: Hyperspectral image classification and unmixing by using ART and SUnSPI techniques. Int. J. Database Theor. Appl. 11(3), 13–28 (2018)

    Google Scholar 

  16. Kumar, M.: Various factors affecting performance of web services. Int. J. Sens. Appl. Control Syst. 3(2), 11–20 (2015)

    Google Scholar 

  17. Alp, S., Özkan, T.: Modelling of multi-objective transshipment problem with fuzzy goal programming. Int. J. Transp. 6(2), 9–20 (2018)

    Article  Google Scholar 

  18. Thirupathi, N.: A review on industrial applications of machine learning. Int. J. Disaster Recovery Bus. Continuity 8, 1–10 (2018)

    Google Scholar 

  19. Patgar, T., Shankaraiah, : The impact of hybrid data fusion based on probabilistic detection identification model for intelligent rail communication highway. Int. J. Sens. Appl. Control Syst. 4(2), 9–20 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hui Zhang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, H. et al. (2020). Winding Deformation Detection of Transformer Based on Sweep Frequency Impedance. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_102

Download citation

Publish with us

Policies and ethics