A cognitive system for fault prognosis in power transformers

Electric Power Systems Research

Volumn 127, Issue , 2015, Pages 109-117

  Sica, Fernando Cortez ^a,b   Guimarães, Frederico Gadelha ^a   De Oliveira Duarte, Ricardo ^a   Reis, Agnaldo J R ^b  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^b FEDERAL UNIVERSITY OF OURO PRETO   (Brazil)

Author keywords

Cognitive systems; Dissolved Gas Analysis; Fault diagnosis; Fault prognosis; Knowledge based systems; Power transformers

Indexed keywords

COGNITIVE SYSTEMS; ELECTRIC POWER SYSTEMS; ELECTRIC TRANSFORMER TESTING; ELECTRIC UTILITIES; EQUIPMENT; FAILURE ANALYSIS; FAULT DETECTION; INTELLIGENT SYSTEMS; KNOWLEDGE BASED SYSTEMS; MAINTENANCE;

CHROMATOGRAPHIC TESTS; DISSOLVED GAS ANALYSES (DGA); DISSOLVED GAS ANALYSIS; EXPENSIVE EQUIPMENTS; FAULT PROGNOSIS; PREDICTIVE MAINTENANCE; PREDICTIVE MAINTENANCE TECHNOLOGIES; REACTIVE MAINTENANCE;

POWER TRANSFORMERS;

EID: 84931265492     PISSN: 03787796     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.epsr.2015.05.014     Document Type: Article

References (54)

1. P.S. Georgilakis Spotlight on Modern Transformer Design 2009 Springer London, UK
2. S. Singh, and M. Bandyopadhyay Dissolved gas analysis technique for incipient fault diagnosis in power transformers: a bibliographic survey Int. IEEE Electr. Insul. Mag. 26 6 2010 41 46
3. P.S. Georgilakis Condition monitoring and assessment of power transformers using computational intelligence Int. J. Electr. Power Energy Syst. 33 10 2011 1784 1785
4. P.S. Georgilakis, and A.G. Kagiannas A novel validated solution for lightning and surge protection of distribution transformers Int. J. Electr. Power Energy Syst. 63 2014 373 381
5. W. Zhan, A.E. Goulart, M. Falahi, and P. Rondla Development of a low-cost self-diagnostic module for oil-immerse forced-air cooling transformers IEEE Trans. Power Deliv. 30 1 2015 129 137
6. M. Duval A review of faults detectable by gas-in-oil analysis in transformers IEEE Electr. Insul. Mag. 18 3 2002 8 17
7. IEEE IEEE guide for the interpretation of gases generated in oil-immersed transformers, IEEE standard c57.104 2008
8. IEC IEC mineral oil-impregnated electrical equipment in service - guide to the interpretation of dissolved and free gases analysis, IEC 60599 2008
9. F. Wan, Q. Zhou, J. Zou, and Z. Gu Using a sensitive optical system to analyze gases dissolved in samples extracted from transformer oil IEEE Electr. Insul. Mag. 30 5 2014 15 22
10. V.G. Arakelian The long way to the automatic chromatographic analysis of gases IEEE Electr. Insul. Mag. 20 6 2004 8 25
11. Y. Goldstein, N.B. Grover, C. Chang, A. Jelli, J. Andre, P. Mark, and A. Goodwin A gas chromatographic air analyzer fabricated on a silicon wafer IEEE Trans. Electron Dev. 26 12 1979 1880 1886
12. A. Depari, A. Flammini, E. Sisinni, A. De Marcellis, G. Ferri, and P. Mantenuto Fast, versatile, and low-cost interface circuit for electrochemical and resistive gas sensor Electr. Eng. 14 2 2004 315 323
13. P. Zylka Electrochemical gas sensors can supplement chromatography based DGA Electr. Eng. 87 3 2005 137 142
14. L. Dong, J. Wright, B. Peters, B.A. Ferguson, F.K. Tittel, and S. McWhorter Compact QEPAS sensor for trace methane and ammonia detection in impure hydrogen Appl. Phys. B 107 2 2012 459 467
15. R. Liao, J. Bian, L. Yang, S. Grzybowski, Y. Wang, and J. Li Forecasting dissolved gases content in power transformer oil based on weakening buffer operator and least square support vector machine-Markov IET Gener. Transm. Distrib. 6 2 2010 142 151
16. S.W. Fei, and Y. Sun Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm Electr. Power Syst. Res. 78 2008 507 514
17. M.Y. Cho, T. Lee, S. Kau, C. Shieh, and C. Chou Fault diagnosis of power transformers using SVM/ANN with clonal selection algorithm for features and kernel parameters selection Proc. International Conference on Innovative Computing, Information and Control 2006 26 30
18. Y.C. Huang, and H.C. Sun Dissolved gas analysis of mineral oil for power transformer fault diagnosis using fuzzy logic IEEE Trans. Dielectr. Electr. Insul. 20 2013 974 981
19. M. Rajabimendi, and E.P. Dadios A hybrid algorithm based on neural-fuzzy system for interpretation of dissolved gas analysis in power transformers Proc. TENCON 2012-2012 IEEE Region 10 Conference 2012 1 6
20. R. Naresh, V. Sharma, and M. Vashisth An integrated neural fuzzy approach for fault diagnosis of transformers IEEE Trans. Power Deliv. 23 4 2008 2017 2024
21. C. Pan, W. Chen, and Y. Yun Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network IET Electr. Power Appl. 2008 71 76
22. W. Chen, C. Pan, Y. Yun, and Y. Liu Wavelet networks in power transformers diagnosis using dissolved gas analysis IEEE Trans. Power Deliv. 24 1 2009 187 194
23. S. Seifeddine, B. Khmais, and C. Abdelkader Power transformer fault diagnosis based on dissolved gas analysis by artificial neural network Proc. First International Conference on Renewable Energies and Vehicular Technology (REVET) 2012 230 236
24. L.G. Castanheira, J.A. Vasconcelos, A.J.R. Reis, P.H.V.M. Raes, and S.A.L. Silva Application of neural networks in the classification of incipient faults in power transformers: a study of case Proc. International Joint Conference on Neural Networks 2011 (IJCNN 2011) 2011 3099 3104
25. P.S. Georgilakis, J.A. Katsigiannis, K.P. Valavanis, and A.T. Souflaris A systematic stochastic petri net based methodology for transformer fault diagnosis and repair actions J. Intell. Robot. Syst. 45 2 2006 181 201
26. W.H. Tang, J.Y. Goulermas, Q.H. Wu, Z.J. Richardson, and J. Fitch A probabilistic classifier for transformer dissolved gas analysis with a particle swarm optimizer IEEE Trans. Power Deliv. 23 2 2008 751 759
27. F. Zhao, and H. Su A decision tree approach for power transformer insulation fault diagnosis Proc. 7th World Congress on Intelligent Control and Automation (WCICA) 2008 6882 6886
28. K.X.P. Lai, and T.R.B.T. Blackburn Descriptive data mining of partial discharge using decision tree with genetic algorithm Proc. Australasian Universities Power Engineering Conference (AUPEC'08) 2008 1 6
29. L.G. Castanheira, J.A. Vasconcelos, A.J.R. Reis, P.H.V.M. aes, and S.A.L. Silva A comparative study of the application of neural networks and decision trees in the classification of incipient faults in power transformers Proc. X Brazilian Conference on Computational Intelligence (X CBIC) 2011 1 6
30. A. Shintemirov, W. Tang, and Q.H. Wu Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming IEEE Trans. Syst. Man Cybern. - Part C: Appl. Rev. 39 1 2009 69 79
31. S. Kim, S. Kim, H. Seo, J. Jung, H. Yang, and M. Duval New methods of DGA diagnosis using IEC TC 10 and related databases. Part 1: application of gas-ratio combinations IEEE Trans. Dielectr. Electr. Insul. 20 2 2013 685 690
32. D. Ozgur-Unluakin, and T. Bilgiç Predictive maintenance using dynamic probabilistic networks Probabilistic Graphical Models'06 2006 239 246
33. T. Brescia, S. Bruno, M. La Scala, S. Lamonaca, G. Rotondo, and U. Stecchi A fuzzy-logic approach to preventive maintenance of critical power transformers 20th International Conference on Electricity Distribution, CIRED 2009 1 5
34. D. Zhou, A. Wang, and C. Li Data requisites for transformer statistical lifetime modelling - Part I. Aging-related failures IEEE Trans. Power Deliv. 28 3 2013 1750 1757
35. V.M. Catterson Prognostic modeling of transformer aging using Bayesian particle filtering Proc. IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP) 2014 413 416
36. J. Zarei, M. Shasadeghi, and A. Ramezani Fault prognosis in power transformers using adaptive-network-based fuzzy inference system J. Intell. Fuzzy Syst. 26 2014 2577 2590
37. D.R. Morais, and J.G. Rolim A hybrid tool for detection of incipient faults in transformers based on the dissolved gas analysis of insulating oil IEEE Trans. Power Deliv. 21 2 2006 673 680
38. G. Vachtsevanos, F.L. Lewis, M. Roemer, A. Hess, and B. Wu Intelligent Fault Diagnosis and Prognosis for Engineering Systems 2006 Wiley
39. L. Liao Design of a reconfigurable prognostics platform for machine tools Expert Syst. Appl. 37 2009 240 252
40. A.N. Srivastava, and J. Han Machine Learning and Knowledge Discovery for Engineering Systems Health Management Chapman & Hall/CRC Data Mining and Knowledge Discovery Series 2011 CRC Press
41. A. Mosallam, K. Medjaher, and N. Zerhouni Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction J. Intell. Manuf. 2014 1 12
42. M. Schwabacher, and K. Goebel A survey of artificial intelligence for prognostics Proc. AAAI Fall Symposium 2007 107 114
43. A. Heng, S. Zhang, A. Tan, and J. Mathew Rotating machinery prognostics: state of the art, challenges and opportunities Mech. Syst. Signal Process. 23 3 2009 724 739
44. B.C.P. Lau, E.W.M. Ma, and M. Pecht Review of offshore wind turbine failures and fault prognostic methods 2012 IEEE Conference on Prognostics and System Health Management (PHM) 2012 IEEE 1 5
45. A. Kusiak, and W. Li The prediction and diagnosis of wind turbine faults Renew. Energy 36 1 2011 16 23
46. A. Kusiak, and A. Verma A data-driven approach for monitoring blade pitch faults in wind turbines IEEE Trans. Sustain. Energy 2 1 2011 87 96
47. N. Julka, A. Thirunavukkarasu, P. Lendermann, B.P. Gan, A. Schirrmann, H. Fromm, and E. Wong Making use of prognostics health management information for aerospace spare components logistics network optimisation Comput. Ind. 62 2011 613 622
48. M. Sharma, and D. Kumar Wishbone bus architecture - a survey and comparison, CoRR abs/1205.1860 2012
49. D.F. Specht A general regression neural network IEEE Trans. Neural Netw. 2 6 1991 568 576
50. L. Breiman Bagging predictors Mach. Learn. 24 2 1996 123 140
51. T. Back Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms 1996 Oxford University Press
52. T.K. Sloat, J.L. Johnson, and G.M.L. Sommerman Gas evolution from transformer oils under high-voltage stress IEEE Trans. Power Appar. Syst. 86 3 1967 374 384
53. J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 1988 Morgan Kaufmann Publishers Inc. San Francisco, CA, USA
54. M. Duval, and A. de Pablo Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases IEEE Electr. Insul. Mag. 17 2 2001 31 41 DEIS Feature Article