BibTex RIS Kaynak Göster
Yıl 2008, Cilt: 8 Sayı: 2, 699 - 706, 02.01.2012

Öz

Kaynakça

  • 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.

FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL

Yıl 2008, Cilt: 8 Sayı: 2, 699 - 706, 02.01.2012

Öz

FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL

Kaynakça

  • 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.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Abdurrahim Akgundogdu Bu kişi benim

Abdulkadir Gozutok Bu kişi benim

Niyazi Kılıc Bu kişi benim

Osman N. Ucan Bu kişi benim

Yayımlanma Tarihi 2 Ocak 2012
Yayımlandığı Sayı Yıl 2008 Cilt: 8 Sayı: 2

Kaynak Göster

APA Akgundogdu, A., Gozutok, A., Kılıc, N., Ucan, O. N. (2012). FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL. IU-Journal of Electrical & Electronics Engineering, 8(2), 699-706.
AMA Akgundogdu A, Gozutok A, Kılıc N, Ucan ON. FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL. IU-Journal of Electrical & Electronics Engineering. Ocak 2012;8(2):699-706.
Chicago Akgundogdu, Abdurrahim, Abdulkadir Gozutok, Niyazi Kılıc, ve Osman N. Ucan. “FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL”. IU-Journal of Electrical & Electronics Engineering 8, sy. 2 (Ocak 2012): 699-706.
EndNote Akgundogdu A, Gozutok A, Kılıc N, Ucan ON (01 Ocak 2012) FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL. IU-Journal of Electrical & Electronics Engineering 8 2 699–706.
IEEE A. Akgundogdu, A. Gozutok, N. Kılıc, ve O. N. Ucan, “FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL”, IU-Journal of Electrical & Electronics Engineering, c. 8, sy. 2, ss. 699–706, 2012.
ISNAD Akgundogdu, Abdurrahim vd. “FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL”. IU-Journal of Electrical & Electronics Engineering 8/2 (Ocak 2012), 699-706.
JAMA Akgundogdu A, Gozutok A, Kılıc N, Ucan ON. FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL. IU-Journal of Electrical & Electronics Engineering. 2012;8:699–706.
MLA Akgundogdu, Abdurrahim vd. “FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL”. IU-Journal of Electrical & Electronics Engineering, c. 8, sy. 2, 2012, ss. 699-06.
Vancouver Akgundogdu A, Gozutok A, Kılıc N, Ucan ON. FAULT DIAGNOSIS OF POWER TRANSFORMER USING NEURO-FUZZY MODEL. IU-Journal of Electrical & Electronics Engineering. 2012;8(2):699-706.