Fault diagnosis of power transformer using neuro-fuzzy model

Istanbul University - Journal of Electrical and Electronics Engineering

Volumn 8, Issue 2, 2008, Pages 699-706

  Akgundogdu, Abdurrahim ^a   Gozutok, Abdulkadir ^b   Kilic, Niyazi ^a   Ucan, Osman N ^a  

^a ISTANBUL UNIVERSITY   (Turkey)

^b Hamitabat Natural Gas Combined Cycle Power Plant   (Turkey)

Author keywords

Anfis; Classification; Dissolved gas in oil analysis; Fault diagnosis; Neuro fuzzy; Power transformer

Indexed keywords

ACETYLENE; BACKPROPAGATION; DISSOLUTION; DISTRIBUTED POWER GENERATION; ELECTRIC FAULT CURRENTS; ELECTRIC INSTRUMENT TRANSFORMERS; ELECTRIC TRANSFORMERS; ETHYLENE; GASES; LIGHTING; METHANE; NEURAL NETWORKS; OIL FILLED TRANSFORMERS; POWER TRANSFORMERS; TRANSFORMER SUBSTATIONS;

ANFIS; CLASSIFICATION; DISSOLVED GAS IN OIL ANALYSIS; FAULT DIAGNOSIS; NEURO-FUZZY;

FUZZY NEURAL NETWORKS;

EID: 56549111736     PISSN: 13030914     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (21)

1. Farag AS, Mohandes M, Al-Shaikh A. Diagnosing Failed Distribution Transformers Using Neural Networks. IEEE Transactions On Power Delivery 16( 4):631 -636, 2001.
2. United States Department of the Interior Bureau of Reclamation. Transformer Maintenance: Facilities Instructions, Standards and Techniques. Reclamation FIST 3-30, Colorado 35-53, 2000.
3. Wang MH. A Novel Extension Method for Transformer Fault Diagnosis. IEEE Transactions On Power Delivery 18(1):164-169, 2003.
4. Saha TK. Review of Modern Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers. IEEE Transactions on Dielectrics and Electrical Insulation 10(5): 903-917, 2003.
5. Zhang Y, Ding X, Liu Y, Griffin PJ. An Artificial Neural Network Approach to Transformer Fault Diagnosis. IEEE Transactions On Power Delivery 1996; 11(4):1836-1841, 1996.
6. J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern. 23 (3), pp. 665-685, 1993.
7. Wang Z, Liu Y, Griffin PJ. Neural Network and Expert System Diagnose Transformer Faults. IEEE Computer Applications in Power 13(l):50-55, 2000.
8. Wang M, Vandermaar AJ, Srivastava KD. Review of Condition Assessment of Power Transformers in Service. IEEE Electrical Insulation Magazine 12-25, 2002.
9. IEC Publication 60599. Mineral oil-impregnated electrical equipment in service-Guide to the interpretation of dissolved and free gases analysis, 1999.
10. Cherkassky V, Fuzzy Inference Systems: A Critical Review, Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, 1998.
11. J.-S.R. Jang, Self-learning fuzzy controllers based on temporal backpropagation, IEEE Trans. Neural Network, vol.3 No.5, 1992.
12. M. Sugeno and G.T. Kang, Structure identification of fuzzy model. Fuzzy Sets and Systems 28: 15-33, 1988.
13. C.-T. Sun, Rulebase structure identification in an adaptive-network-based fuzzy inference system. IEEE Trans. Fuzzy Systems, vol.2, no., pp. 64-73, 1994.
14. Takagi T, Sugeno M., Derivation of fuzzy control rules from human operator's control actions. In: Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis,pp. 55-60, 1983.
15. Ubeyli ED, Guler I., Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems Computers In Biology And Medicine 35 (5): 421-433, 2005.
16. Takagi T, Sugeno M., Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics, vol.SMC-15, pp.116-132, 1985.
17. J. S. Bridle , "Probabilistic Interpretation of Feedforward Classification Network Outputs with Relationships to Statistical Pattern Recognition," In F. Fogelman-Soulie and J. Herault (eds.) Neuro-computing: Algorithms, Architectures and Applications, NATO ASI Series in Systems and Computer Science, Springer, 227-236. New York, 1990.
18. D.S. Broomhead and D. Lowe, Multivariable functional interpolation and adaptive networks. In: Complex Syst. 2, pp. 321-355, 1988.
19. Bersini H., Bontempi G. Now comes the time to defuzzify neuro-fuzzy models. Fuzzy Sets and Systems, 90,2. pp. 161-170, 1997.
20. Bontempi G., Bersini H., Birattari M., The local paradigm for modeling and control: from neuro-fuzzy to lazy learning. Fuzzy Sets and Systems, 121. pp.59-72, 2001.
21. Duval M, DePablo A., Interpretation of Gas-In-Oil Analysis Using New IEC Publication 60599 and IEC TC 10 Databases. IEEE Electrical Insulation Magazine ; 17(2): 31-41,2001.