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Factorial analysis for ageing assessment of in-service transformers

Factorial analysis for ageing assessment of in-service transformers

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Condition monitoring data in the form of oil test results are important for transformer ageing assessment and asset management. Analysis of variance-based factorial analysis was demonstrated as a data mining technique for identifying the influence of different factors on transformer ageing. Using a UK oil test database of 33/11(or 6.6) kV transformers, studies were performed to assess the influence of in-service age, manufacturer, year of manufacture, load and environment on the measured oil acidity. This methodology can reduce the workload of manually performing pairwise comparisons to understand the influence of each factor. In addition, it can also consider any interaction effect present among the factors. As a demonstration, influence of the year of manufacture on oil acidity was revealed, which was confirmed by identifying a change in design for late 1960s units and an early degradation phenomenon for late 1980s units.

References

    1. 1)
      • 1. Tee, S.J., Liu, Q., Wang, Z.D., et al: ‘An early degradation phenomenon identified through transformer oil database analysis’, IEEE Trans. Dielectr. Electr. Insul., 2016, 23, (3), pp. 14351443.
    2. 2)
      • 2. Wang, M., Vandermaar, A.J., Srivastava, K.D.: ‘Review of condition assessment of power transformers in service’, IEEE Electr. Insul. Mag., 2002, 18, pp. 1225.
    3. 3)
      • 3. Tee, S.J., Liu, Q., Wang, Z.D., et al: ‘Seasonal influence on moisture interpretation for transformer ageing assessment’, IEEE Electr. Insul. Mag., 2016, 32, (3), pp. 2937.
    4. 4)
      • 4. Tee, S.J., Liu, Q., Wang, Z.D.: ‘Insulation condition ranking of transformers through principal component analysis and analytic hierarchy process’, IET Gener. Transm. Distrib., 2017, 11, (1), pp. 110117.
    5. 5)
      • 5. Wang, Z.D., Liu, Q., Tee, S.J., et al: ‘Ageing assessment of transformers through oil test database analyses and alternative diagnostic techniques’. Presented at theCIGRE SC A2 Colloquium, Shanghai, China, 2015.
    6. 6)
      • 6. Wang, D., Wang, Z.D., Turnham, V., et al: ‘Oil acidity analyses for condition assessment of transformers in a distribution network’. Presented at the19th Int. Symp. High Voltage Engineering, Pilsen, Czech Republic, 2015.
    7. 7)
      • 7. Kohtoh, M., Kaneko, S., Okabe, S., et al: ‘Aging effect on electrical characteristics of insulating oil in field transformer’, IEEE Trans. Dielectr. Electr. Insul., 2009, 16, pp. 16981706.
    8. 8)
      • 8. Tee, S.J., Liu, Q., Wang, Z.D., et al: ‘Practice of IEC 60422 in ageing assessment of in-service transformers’. Presented at the19th Int. Symp. High Voltage Engineering, Pilsen, Czech Republic, 2015.
    9. 9)
      • 9. Jahromi, A., Piercy, R., Cress, S., et al: ‘An approach to power transformer asset management using health index’, IEEE Electr. Insul. Mag., 2009, 25, pp. 2034.
    10. 10)
      • 10. Abu-Elanien, A.E.B., Salama, M.M.A., Ibrahim, M.: ‘Calculation of a health index for oil-immersed transformers rated under 69 kV using fuzzy logic’, IEEE Trans. Power Deliv., 2012, 27, pp. 20292036.
    11. 11)
      • 11. Ashkezari, A.D., Hui, M., Saha, T.K., et al: ‘Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers’, IEEE Trans. Dielectr. Electr. Insul., 2013, 20, (3), pp. 965973.
    12. 12)
      • 12. Abu-Elanien, A.E.B., Salama, M.M.A., Ibrahim, M.: ‘Determination of transformer health condition using artificial neural networks’. Int. Symp. Innovations in Intelligent Systems and Applications (INISTA), Istanbul, Turkey, June 2011, pp. 15.
    13. 13)
      • 13. Huang, Y.C., Huang, C.M., Sun, H.-C.: ‘Data mining for oil-insulated power transformers: an advanced literature survey’, Wiley Interdiscip. Rev., Data Min. Knowl. Discov., 2012, 2, pp. 138148.
    14. 14)
      • 14. Liu, J., Zheng, H., Zhang, Y., et al: ‘Grey relational analysis for insulation condition assessment of power transformers based upon conventional dielectric response measurement’, Energies, 2017, 10, (10), pp. 116.
    15. 15)
      • 15. Zheng, H., Zhang, Y., Liu, J., et al: ‘A novel model based on wavelet LS-SVM integrated improved PSO algorithm for forecasting of dissolved gas contents in power transformers’, Electr. Power Syst. Res., 2018, 155, pp. 196205.
    16. 16)
      • 16. Montgomery, D.C.: ‘Design and analysis of experiments’ (John Wiley & Sons, Hoboken, NJ, USA, 2013, 8th edn.).
    17. 17)
      • 17. Maxwell, S.E., Delaney, H.D.: ‘Designing experiments and analyzing data: a model comparison perspective’ (Wadsworth Publishing Company, Belmont, CA, USA, 1989).
    18. 18)
      • 18. Emsley, A.M., Xiao, X., Heywood, R.J., et al: ‘Degradation of cellulosic insulation in power transformers. Part 3: effects of oxygen and water on ageing in oil’, IEE Proc., Sci. Meas. Technol., 2000, 147, pp. 115119.
    19. 19)
      • 19. IEC 60422: ‘Mineral insulating oils in electrical equipment – supervision and maintenance guidance’, (4.0 edn.), IEC – Fluids for Electrotechnical Applications Technical Committee, 2013.
    20. 20)
      • 20. CIGRE Brochure 393: ‘Thermal performance of transformers’, 2009.
    21. 21)
      • 21. IEC 60076: ‘Power transformers – Part 7: loading guide for oil-immersed power transformers’, IEC, 2005.
    22. 22)
      • 22. Wakelen, R.: ‘DNO common network asset indices methodology’, 2015.
    23. 23)
      • 23. Green, S.B., Salkind, N.J.: ‘Using SPSS for Windows and Macintosh: analyzing and understanding data: Pearson’, 2016.
    24. 24)
      • 24. Tabachnick, B.G., Fidell, L.S.: ‘Using multivariate statistics: Pearson education’, 2013.
    25. 25)
      • 25. Montgomery, D.C.: ‘Applied statistics and probability for engineers’ (John Wiley & Sons, Hoboken, NJ, USA, 2011, 5th edn.).
    26. 26)
      • 26. Wang, D.M., Patel, B., Wang, Z.D., et al: ‘Early ageing of 33 KV transformers in a distribution network’. IET Int. Conf. Resilience of Transmission and Distribution Networks (RTDN), Birmingham, UK, September 2015, pp. 15.
    27. 27)
      • 27. Heathcote, M.J.: ‘The J & P transformer book’ (Newnes, Oxford, 2007, 13th edn.).
    28. 28)
      • 28. Report and accounts together with the report of the north western electricity consultative council for the year ended’, The North Western Electricity Board, 1965.
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