An ANFIS Based Fuzzy Synthesis Judgment for Transformer Fault Diagnosis

  • Hongsheng Su
  • Xiuhua Wang
  • Hao Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)


In conventional Fuzzy Synthesis Judgment (FSJ), the shape parameters of fuzzy membership functions are subjectively confirmed by man beforehand, and fuzzy relationship matrix is also established in advance, thus, the applicable object of fuzzy inference is a confirmed system beforehand. However, while the size of data set is larger, and the space distribution of the samples is more complicated, it is difficult to establish such a model to trace the characteristic change of input-output data. But Adaptive Neuro-Fuzzy Inference System (ANFIS) can resolve the above problem better. However, ANFIS is applied to realize FSJ, there two problems required to be improved. One is the innovation of the firing strength of a rule; the other is to change the learning of TSK linear parameters as the learning of fuzzy relationship matrix. In this paper the proposed ANFIS not only resolves the two problems, but also can be like FSJ to implement reasoning, and learn the shape parameters of fuzzy subjection function and fuzzy relation matrix from the given data set, automatically. In the end, transformer fault diagnosis results indicate that the proposed method is effective and ubiquitous, and is an ideal pattern classifier.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cao, D.K.: Analysis Diagnosis and Faults Checking to the Gases in Transformer oil, 1st edn. Chinese Electrical Press, Beijing (2005)Google Scholar
  2. 2.
    Zhang, Y.: An Artificial New Network Approach to Transformer Fault Diagnosis. IEEE Trans. on Power Delivery 11, 1836–1841 (1996)CrossRefGoogle Scholar
  3. 3.
    Su, H.S., Li, Q.Z., Dang, J.W.: A Hybrid Bayesian Optimal Classifier Based on Neuro-Fuzzy Logic. In: Jiao, L., Wang, L., Gao, X.-b., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 341–350. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Huang, C.L., Huang, Y.C., Yang, H.T.: Developing a New Transformer Fault Diagnosis System through Evolution Fuzzy Logic. IEEE Trans. on Power Delivery 12, 761–767 (1997)CrossRefGoogle Scholar
  5. 5.
    Ratshtein, A.P., Rakytyanska, H.B.: Diagnostic Problem Solving Using Fuzzy Relations. IEEE Trans. on Fuzzy Systems 3, 664–675 (2008)CrossRefGoogle Scholar
  6. 6.
    Zhang, W.L., Sun, C.X.: Study on Fault Diagnosis of Transformer DGA Method with Fuzzy Multi-Criteria Analysis. Trans. Of China Electrotechnical Society 1, 51–54 (1998)Google Scholar
  7. 7.
    Mantas, C.J., Puche, J.M.: Artificial NN Are Zero-Order TSK Fuzzy System. IEEE Trans. on Fuzzy Systems 3, 630–643 (2008)CrossRefGoogle Scholar
  8. 8.
    Du, Y.: Easy Methods to Determine the Characteristics of Breakdown According to Gas Dissolving into Oil of Transformers. High Voltage Engineering 4, 61–63 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hongsheng Su
    • 1
  • Xiuhua Wang
    • 1
  • Hao Chen
    • 1
  1. 1.School of Automation and Electrical EngineeringLanzhou Jiaotong UniversityLanzhouP.R. China

Personalised recommendations