Estimation of the Ageing Condition of Oil-Filled Transformers Based on the Oil Parameters Using a Novel Fuzzy Logic Algorithm

  • Tobias KinkeldeyEmail author
  • Tobias Münster
  • Peter Werle
  • Suwarno
  • Kai Hämel
  • Jörg Preusel
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 598)


The condition of the transformer insulation determines the remaining life of the transformer. Over the transformer service life, both liquid and solid insulation undergoes a continuous aging process under electrical, chemical, mechanical and thermal stresses. The insulating liquid of a transformer can be reconditioned or replaced; however, this is not the case for the cellulose insulation. Therefore, the condition of the paper insulation is the major factor for determining the aging status of a transformer. To assess the paper condition, the common method is to measure the degree of polymerization (DP) of the paper insulation as a significant parameter. This method is destructive as it requires a sample of paper from inside of the transformer. Therefore, it could not be applied for transformers in operation. There are several approaches to approximate the DP value without direct measurements of paper samples.

This research presents an improved method based on a fuzzy logic system for the estimation of the DP based on dissolved gases and chemical parameters of the liquid insulation. The algorithm is developed to create the rules for the use of fuzzy sets based on the information gain extracted by the entropy of the laboratory measurement data. This algorithm employs entropy to examine the sample homogeneity. Entropy is a measure of information theory that can determine the dataset characteristics concerning impurity and homogeneity. The algorithm uses fuzzy sets of oil parameters like Acidity, Interfacial Tension (IFT), Carbon Dioxide (CO2) and Carbon Monoxide (CO) and the breakdown voltage (BDV) for determination of the DP value.


Fuzzy logic algorithm DP value Paper ageing 



The authors would like to express their graduate to GRIDINSPECT GmbH and AiF/ZiM for the financial support as well as Weidmann Electrical Technology AG for the support with insulation materials and Analysen Service GmbH Leipzig for the analysis. Furthermore, the authors would like to thank the company ABB for the provision of comparative data.


  1. 1.
    Oommen, T., Cellulose, A.P.: Insulation in oil filled power transformer: part II-maintaining insulation integrity and life. IEEE Electr. Insul. Mag. 22(2), 5–14 (2006)CrossRefGoogle Scholar
  2. 2.
    CIGRE 227 - Life Management Techniques for Power Transformer. CIGRE (2003)Google Scholar
  3. 3.
    CIGRE: Tutorial of CIGRE WG A2.37, Transformer Reliability Survey, Technical Brochure 642Google Scholar
  4. 4.
    Balzer, G., Schorn, Chr.: Asset Management für Infrastrukturanlagen - Energie und Wasser. Springer (2010). ISBN 978-3-642-05391-7Google Scholar
  5. 5.
    Kinkeldey, T., Münster, T., Werle, P., Nasution, E., Suwarno, S., Hämel, K., Preusel, J.: Estimation of the degree of polymerization (DP) of oil-paper insulated transformers using a novel fuzzy logic algorithm. In: VDE Hochspannungstechnik (ETG) 2018, Berlin, Germany (2018)Google Scholar
  6. 6.
    Kinkeldey, T., Werle, P., Münster, T.: Investigation on aging markers of thermally accelerated aged oil-impregnated papers. In: ISH in Buenos Aires, Argentina (2017)Google Scholar
  7. 7.
    Münster, T., Kinkeldey, T., Werle, P., Hämel, K., Preusel J.: Investigation on the accelerated ageing behavior of oil-paper-insulation using different insulation oils. In: VDE-Hochspannungstechnik (ETG) 2018, Berlin, Germany (2018)Google Scholar
  8. 8.
    Münster, T., Kinkeldey, T., Werle, P., Hämel, K., Preusel, J.: Investigation on ageing parameters of a thermally accelerated aged paper-oil-insulation in a hermetically sealed system. In: CMD 2018, Perth, Australia (2018)Google Scholar
  9. 9.
    IEC Std 60422: Mineral Insulation Oil in Electrical Equipment: IEC (2013)Google Scholar
  10. 10.
    IEEE Std C57.104: IEEE Guide for the Interpretation of Gasses Generated in Oil-Immersed Transformers. IEEE, New York (2009)Google Scholar
  11. 11.
    DIN EN 60450: Messung des durchschnittlichen viskosimetrischen poly-merisationsgrades von neuen und gealterten cellulosehaltigen Elektroisolierstoffen (2008)Google Scholar
  12. 12.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques (2012). ISBN 978-0-12-381479-1Google Scholar
  13. 13.
    Runkler, T.A.: Data Mining (2010). ISBN 978-3-8348-0858-5Google Scholar
  14. 14.
    Chowdary, M.L., Singh, A., Bansal, R., Jarial, R.K.: A fuzzy logic approach to analyze change in dissolved decay content in correlation with density, IFT and acidity of transformer oil. IEEE Xplore (2005)Google Scholar
  15. 15.
    Zadeh, L.A.: Fuzzy Logic, University of California, Barkeley. IEEE Xplore (1988)Google Scholar
  16. 16.
    Zadeh, L.A.: Fuzzy sets, Department of Electrical Engineering and Electronics Research Laboratory, University of California, Berkeley (1965)Google Scholar
  17. 17.
    Ortiz Fernández, F., Fernández Diego, C., Santisteban Díaz, A., Delgado San Román, F., Ortiz Fernández, A.: Estimating the age of power transformers using the concentration of furans in dielectric oil. In: International Conference on Renewable Energies and Power Quality (ICREPQ 2016) (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tobias Kinkeldey
    • 1
    Email author
  • Tobias Münster
    • 1
  • Peter Werle
    • 1
  • Suwarno
    • 2
  • Kai Hämel
    • 2
  • Jörg Preusel
    • 3
  1. 1.Institute of Electric Power Systems, Division of High Voltage Engineering and Asset Management, Schering-InstituteLeibniz Universität HannoverHannoverGermany
  2. 2.School of Electrical Engineering and InformaticsInstitute of TechnologyBandungIndonesia
  3. 3.GRIDINSPECT GmbHFeldatalGermany

Personalised recommendations