Power transformers are of vital importance for the stable operation of power system. Hence, to grasp transformers’ health condition is of imminent importance. However, the evaluation of transformers’ health condition is bound with fuzzy, uncertainty, and even conflict information. To address these problems, a novel evaluation model for transformer, by integrating the merits of fuzzy set theory, fuzzy analytical hierarchical process (AHP), and modified weighted averaging combination, is proposed in this study. The evaluation model contains four factors: dissolved gas analysis, electrical testing, oil testing, and miscellaneous factors, as well as 20 indices. The evaluation process consists of three steps. First, the fuzzy set theory is used to obtain assessment grades for each quantified indices. Second, the fuzzy AHP using fuzzy preference programming, which extends classical AHP and can better tackle the uncertainty existed in the comparison matrix given by experts, is introduced to calculate the weights of indices. Finally, the assessment is processed through the modified weighted averaging combination, which is able to handle the conflicts among evidences. The proposed method is verified by evaluating a realistic transformer, and the results of field examples indicate that the method can evaluate the transformer health condition effectively.

References

1. 1)
  - 13. Al-Harbi, K.M.A.S.: ‘Application of the AHP in project management’, Int. J. Project Manage., 2001, 19, (1), pp. 19–27 (doi: 10.1016/S0263-7863(99)00038-1).
2. 2)
  - 26. Deng, Y., Shi, W.K., Zhu, Z.F., et al: ‘Combining belief functions based on distance of evidence’, Decis. Support Syst., 2004, 38, (4), pp. 489–493.
3. 3)
  - 24. Hall, D.L., Llinas, J.: ‘Handbook of multisensor data fusion’ (CRC Press, Boca Raton, 2001).
4. 4)
  - 26. Mikhailov, L., Tsvetinov, P.: ‘Evaluation of services using a fuzzy analytic hierarchy process’, Appl. Soft Comput., 2004, 5, (1), pp. 23–33 (doi: 10.1016/j.asoc.2004.04.001).
5. 5)
  - 16. Chang, D.Y.: ‘Applications of the extent analysis method on fuzzy AHP’, Eur. J. Oper. Res., 1996, 95, (3), pp. 649–655 (doi: 10.1016/0377-2217(95)00300-2).
6. 6)
  - 21. Dempster, A.P.: ‘Upper and lower probabilities induced by a multivalued mapping’, Ann. Math. Stat., 1967, 38, (2), pp. 325–339 (doi: 10.1214/aoms/1177698950).
7. 7)
  - 18. Song, Z., Zhu, H., Jia, G., et al: ‘Comprehensive evaluation on self-ignition risks of coal stockpiles using fuzzy AHP approaches’, J. Loss Prev. Process Ind., 2014, 32, pp. 78–94 (doi: 10.1016/j.jlp.2014.08.002).
8. 8)
  - 27. Zhu, L., Shi, D., Duan, X.: ‘Evidence theory-based fake measurement identification and fault-tolerant protection in digital substations’, IET Gener. Transm. Distrib., 2011, 5, (1), pp. 119–126 (doi: 10.1049/iet-gtd.2010.0057).
9. 9)
  - 28. Yasinzadeh, M., Seyedi, H.: ‘Fake measurement identification in power substations based on correlation between data and distance of the evidence’, IET Gener. Transm. Distrib., 2015, 9, (5), pp. 503–512 (doi: 10.1049/iet-gtd.2014.0629).
10. 10)
  - 15. Chen, J.F., Hsieh, H.N., Do, Q.H.: ‘Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach’, Appl. Soft Comput., 2015, 28, pp. 100–108 (doi: 10.1016/j.asoc.2014.11.050).
11. 11)
  - 19. Wang, Y.-M., Luo, Y., Hua, Z.: ‘On the extent analysis method of fuzzy AHP and its application’, Eur. J. Oper. Res., 2008, 186, (2), pp. 735–747 (doi: 10.1016/j.ejor.2007.01.050).
12. 12)
  - 1. Scatiggio, F., Pompili, M.: ‘Health index: the TERNA's practical approach for transformers fleet management’. Proc. IEEE Electrical Insulation Conf., Ottawa, ON, Canada, June 2013, pp. 178–182.
13. 13)
  - 5. Chen, W., Pan, C., Yun, Y., et al: ‘Wavelet networks in power transformers diagnosis using dissolved gas analysis’, IEEE Trans. Power Deliv., 2009, 24, (1), pp. 187–194 (doi: 10.1109/TPWRD.2008.2002974).
14. 14)
  - 22. Shafer, G.: ‘A mathematical theory of evidence’ (Princeton University Press, Princeton, 1976).
15. 15)
  - 2. Arshad, M., Mlslam, S.: ‘Significance of cellulose power transformer condition assessment’, IEEE Trans. Dielectr. Electr. Insul., 2011, 18, (5), pp. 1591–1598 (doi: 10.1109/TDEI.2011.6032829).
16. 16)
  - 14. Liao, R.J., Zheng, H.B., Grzybowski, S., et al: ‘An integrated decision-making model for condition assessment of power transformers using fuzzy approach and evidential reasoning’, IEEE Trans. Power Deliv., 2011, 26, (2), pp. 1111–1118 (doi: 10.1109/TPWRD.2010.2096482).
17. 17)
  - 33. Dhote, N.K., Helonde, J.B.: ‘Improvement in transformer diagnosis by DGA using fuzzy logic’, J. Electr. Eng. Technol., 2014, 9, (2), pp. 615–621 (doi: 10.5370/JEET.2014.9.2.615).
18. 18)
  - 36. Liu, C.H., Lin, T.B., Yao, L., et al: ‘Integrated power transformer diagnosis using hybrid fuzzy dissolved gas analysis’, IEEJ Trans. Electr. Electron. Eng., 2015, 10, (6), pp. 689–698 (doi: 10.1002/tee.22148).
19. 19)
  - 20. Rezaei, J., Ortt, R., Scholten, V.: ‘An improved fuzzy preference programming to evaluate entrepreneurship orientation’, Appl. Soft Comput., 2013, 13, (13), pp. 2749–2758 (doi: 10.1016/j.asoc.2012.11.012).
20. 20)
  - 18. Jousselme, A.L., Grenier, D., Bosse, E.: ‘A new distance between two pieces of evidence’, Inf. Fusion, 2001, 2, (2), pp. 91–101 (doi: 10.1016/S1566-2535(01)00026-4).
21. 21)
  - 29. Jiang, J., Li, X., Zhou, Z.-J., et al: ‘Weapon system capability assessment under uncertainty based on the evidential reasoning approach’, Expert Syst. Appl., 2011, 38, pp. 13773–13784.
22. 22)
  - 17. Wang, X., Chan, H.K., Li, D.: ‘A case study of an integrated fuzzy methodology for green product development’, Eur. J. Oper. Res., 2015, 241, (1), pp. 212–223 (doi: 10.1016/j.ejor.2014.08.007).
23. 23)
  - 4. Singh, J., Sood, Y.R., Jarial, R.K.: ‘Condition monitoring of power transformers-bibliography survey’, IEEE Electr. Insul. Mag., 2008, 24, pp. 11–25 (doi: 10.1109/MEI.2008.4591431).
24. 24)
  - 34. Mani, G., Jerome, J.: ‘Intuitionistic fuzzy expert system based fault diagnosis using dissolved gas analysis for power transformer’, J. Electr. Eng. Technol., 2014, 9, (6), pp. 2058–2064 (doi: 10.5370/JEET.2014.9.6.2058).
25. 25)
  - 10. Saaty, T.L.: ‘The analytic hierarchy process: planning, priority setting, resources allocation’ (McGraw-Hill, New York, NY, 1980).
26. 26)
  - 25. Zadeh, L.A.: ‘A simple view of the Dempster–Shafer theory of evidence and its implication for the rule of combination’, AI Mag., 1986, 7, (2), pp. 85–90.
27. 27)
  - 30. Ng, C.Y., Chunah, K.B.: ‘Evaluation of design alternatives’ environmental performance using AHP and approaches’, IEEE Syst. J., 2014, 8, (4), pp. 1182–1189 (doi: 10.1109/JSYST.2013.2258217).
28. 28)
  - 114. Ashkezari, A.D., Ma, H., 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. 965–973 (doi: 10.1109/TDEI.2013.6518966).
29. 29)
  - 38. Jaynes, E.T.: ‘The minimum entropy production principle’, Annu. Rev. Phys. Chem., 1980, 31, pp. 579–601 (doi: 10.1146/annurev.pc.31.100180.003051).
30. 30)
  - 11. Deng, H.: ‘Multicriteria analysis with fuzzy pairwise comparison’, Int. J. Approx. Reason., 1999, 21, pp. 215–231 (doi: 10.1016/S0888-613X(99)00025-0).
31. 31)
  - 17. 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, (4), pp. 2029–2036 (doi: 10.1109/TPWRD.2012.2205165).
32. 32)
  - 12. Gaudenzi, B., Borghesi, A.: ‘Managing risks in the supply chain using the AHP method’, Int. J. Logist. Manage., 2006, 17, (1), pp. 114–136 (doi: 10.1108/09574090610663464).
33. 33)
  - 4. Lin, P.-C., Gu, J.-C., Yang, M.-T.: ‘Adaptive neuro-fuzzy inference system for monitoring the surface condition of polymeric insulators using harmonic content’, IET Gener. Transm. Distrib., 2014, 8, (7), pp. 1244–1253 (doi: 10.1049/iet-gtd.2013.0124).
34. 34)
  - 7. Tang, W.H., Spurgen, K., Wu, Q.H., et al: ‘An evidence reasoning approach to transformer condition assessments’, IEE Trans. Power Deliv., 2004, 19, (4), pp. 1696–1703 (doi: 10.1109/TPWRD.2003.822542).
35. 35)
  - 12. Xu, Z., Liu, M., Yang, G., et al: ‘Application of interval analysis and evidence theory to fault location’, IET Electr. Power Appl., 2009, 3, (1), pp. 77–84 (doi: 10.1049/iet-epa:20070439).
36. 36)
  - 32. Zeng, F.P., Dong, Y.I., Tang, J.: ‘Feature extraction and severity assessment of partial discharge under protrusion defect based on fuzzy comprehensive evaluation’, IET Gener. Transm. Distrib., 2015, 9, (16), pp. 2493–2500 (doi: 10.1049/iet-gtd.2015.0403).
37. 37)
  - 31. IEEE Std. C57. 140-2008.: ‘IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers’, 2008.
38. 38)
  - 35. Rao, U.M., Reddy, M., Arial, R.K.: ‘Fuzzy logic based system to diagnose internal faults of power transformer’. Int. Conf. on Communication and Industrial Application (ICCIA), 2011, pp. 1–5.

Research on multi-attribute decision-making in condition evaluation for power transformer using fuzzy AHP and modified weighted averaging combination

References

Related content