Abdurrahim AKGUNDOGDU, Abdulkadir GOZUTOK, Niyazi KILIC, Osman N. UCAN

FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL

Keywords:

Fault Diagnosis, ANFIS Dissolved gas in oil analysis, Classification, Power transformer, Neuro-Fuzzy,

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Farag AS, Mohandes M, Al-Shaikh A. Diagnosing Failed Distribution Transformers Using Neural Networks. IEEE Transactions On Power Delivery 16( 4):631-636, 2001.
United States Department of the Interior Bureau of Reclamation. Transformer Maintenance: Facilities Instructions, Standards and Techniques. Reclamation FIST 30, Colorado 35-53, 2000.
Wang MH. A Novel Extension Method for Transformer Fault Diagnosis. IEEE Transactions On Power Delivery 18(1):164- , 2003.
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.
Zhang Y, Ding X, Liu Y, Griffin PJ. An Artificial Neural Network Approach to Transformer Fault Diagnosis. IEEE Transactions On Power Delivery 1996; (4):1836-1841, 1996.
J.-S.R. Jang, ANFIS: Adaptive-network- based fuzzy inference system, IEEE Trans. Syst. Man Cybern. 23 (3) , pp. 665-685, 1993.
Wang Z, Liu Y, Griffin PJ. Neural Network and Expert System Diagnose Transformer Faults. IEEE Computer Applications in Power 13(1):50-55, 2000.
Wang M, Vandermaar AJ, Srivastava KD. Review of Condition Assessment of Power Transformers in Service. IEEE Electrical Insulation Magazine 12-25, 2002.
IEC Publication 60599. Mineral oil- impregnated electrical equipment in service
Guide to the interpretation of dissolved and free gases analysis, 1999.
Cherkassky V, Fuzzy Inference Systems: A Critical Review, Computational Intelligence: Soft Computing and Fuzzy- Neuro Integration with Applications, 1998.
J.-S.R. Jang, Self-learning fuzzy controllers based on temporal backpropagation, Network, vol.3 No.5, 1992.
M. Sugeno and G.T. Kang , Structure identification of fuzzy model. Fuzzy Sets and Systems 28: 15-33, 1988.
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.
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- , 1983.
Ubeyli ED, Guler I., Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems Computers In Biology And Medicine 35 (5): 433, 2005.
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- , 1985.
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.
D.S. Broomhead and D. Lowe ,Multivariable functional interpolation and adaptive networks. In: Complex Syst. 2, pp. 355, 1988.
Bersini H., Bontempi G. Now comes the time to defuzzify neuro-fuzzy models. Fuzzy Sets and Systems, 90,2. pp. 161-170, 1997.
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.
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): 41, 2001.