Application of Artificial Intelligence Techniques for Dissolved Gas Analysis of Transformers-A Review

Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

Abstract:

The gases generated in oil filled transformers can be used for qualitative determination of incipient faults. The Dissolved Gas Analysis has been widely used by utilities throughout the world as the primarily diagnostic tool for transformer maintenance. In this paper, various Artificial Intelligence Techniques that have been used by the researchers in the past have been reviewed, some conclusions have been drawn and a sequential hybrid system has been proposed. The synergy of ANN and FIS can be a good solution for reliable results for predicting faults because one should not rely on a single technology when dealing with real–life applications.

Keywords: Dissolved Gas Analysis, Artificial IntelligenceTechniques, Incipient Faults, Transformer Fault Diagnosis, andHybrid Systems.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081099

References:

[1] IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers, ANSI/IEEE Standard C57.104-1991.
[2] "Mineral Oil Impregnated Electrical Equipment in service. Guide to the interpretation of Dissolved Free Gas Analysis". CEI, IEC 60599, second edition 1999-03.
[3] M. Duval, "A review of fault detectable by gas-in-oil analysis in transformers," IEEE electrical Insulation Magazine, Vol. 10, No. 3, pp 8-17, 2002.
[4] K. F. Thang, R. K. Aggarwal, A. J. MacGrail and D. G. Esp, "Application of Self-Organizing Map Algorithms for Analysis and Interpretation of Dissolved Gases in Power Transformers," Power Engineering Society Summer Meeting, 2001. IEEE, Vancouver, BC, Canada,07 /15/2001 Vol. 3,pp 1881-1886.
[5] K. F. Thang, R. K. Aggarwal, D. G. Esp, and A. J. MacGrail, "Statistical and Neural Analysis of Dissolved Gases in Power Transformers," Eighth International Conference on Dielectric Materials, Measurements and Applications, 2000. (IEE Conf. Publ. No. 473) 09/17-21/2000, Edinburgh, UK, pp 324-329.
[6] N. Yadaiah and Nagireddy Ravi, "Fault Detection Techniques for Power Transformers," Industrial & Commercial Power Systems Technical Conference, 2007. ICPS 2007. IEEE/IAS Vol.6 , Issue ,11 May 2007, pp:1 - 9.
[7] Diego Roberto Moaris and Jacqueline Gisete Rolim, "An artificial Neural Network Approach to Transformer Fault Diagnosis," IEEE Transactions on Power Delivery , Vol. 11, No. 4, April 1996, pp 1836- 1841.
[8] IS 10593:2006 Indian Standard "Mineral Oil-impregnated Electrical Equipment in service-Guide to interpretation of dissolved and free gas analysis." ( Second Revision).
[9] C. F Lin, J. M. Ling and C. L. Huang "An Expert System for Transformers Fault Diagnosis Using Dissolved Gas Analysis", IEEE Transactions on Power Delivery, Vol. 8, No. 1, January 1993, pp 231- 238.
[10] K. Tomsovic, M. Tapper and T. Ingvarsson, "A Fuzzy Information Approach to Integrate Different Transformer Diagnostic Methods," IEEE Transactions on Power Systems, Vol. 08, No. 3, July 1993, pp 1638-1646.
[11] Yann-Chang Huang, Hong-Tzer Yang and Ching Lien Huang, "Developing a new transformer Fault diagnostic System through Evolutionary Fuzzy Logic," IEEE Transactions on Power Delivery, Vol. 12, No. 2, April 1997, pp 761-767.
[12] Q. Su, C. Mi, L. L. Lai and P Austin, "A Fuzzy Dissolved Gas Analysis Method for Diagnosis of Multiple Incipient Faults in a Transformer," IEEE Transactions on Power Systems, Vol. 15, No. 2, May 2000, pp 593-598.
[13] N.A Muhamad, B. T Phung and T R Blackburn, "Comparative study and analysis of DGA methods for Mineral Oil Using Fuzzy Logic," Proceedings of 8th International Power Engineering Conference (IPEC2007), pp 1301-1306.
[14] Hong-Tzer Yang, Chiung Chou Liou and Jeng Hong Chow, "Fuzzy Learning Vector Quantization Networks for Power Transformer Condition Assessment, " IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 08, No. 1, February 2001, pp 143-149.
[15] O. Vanegas, Y. Mizuno, K. Naito , and T Kamiya, " Diagnosis of Oil Insulated Power Apparatus using Neural Network Simulation," IEEE Transactions on Dielectrics and Electrical Insulations, Vol 4, No3, June 1997, pp 290-299.
[16] Hong- Tzer Yang and Yann-Chang Huang, "Intelligent Decision Support for Diagnosis of Incipient Transformer Faults Using Self- Organizing Polynomial Networks," IEEE Transactions on Power Systems Vol 13, No: 3, August 1998, pp 946-952.
[17] J. L. Guardado, J. L. Naredo, P. Moreno and C. R. Fuerte, "A comparative study of Neural Network Efficiency in Power Transformer Diagnosis Using Dissolved Gas Analysis," IEEE Transactions on Power Delivery, Vol. 16, No. 4, October 2001, pp 643-647.
[18] A. S. Farag, M. Mohandes and Ali Al Shaikh, "Diagnosing Failed Distribution Transformers using Neural Networks," IEEE Transactions on Power Delivery, Vol. 16, No. 4, October 2001, pp 631-636.
[19] Y. C. Huang, H.T. Yang, and K. Y. Huang, "Abductive Network Modelbased Diagnostic System of Power Transformer Incipient Fault Detection," IEE Proceedings on Generation Transmission Distribution, Vol. 149, No 3, May 2002.
[20] M. H. Wang, "Extension Neural Network for Power Transformer Incipient Fault Diagnosis," IEE Proceeding Generation Transmission Distribution, Vol 150, No. 6, Nov 2003, pp 679-656.
[21] Zhan Wang, JiWei guo, Jing Dong Xie, and Guoqing Tang, "An Introduction of a Condition Monitoring System of Electrical Equipment." Proceedings of 2001 International Symposium on Electrical Insulating Materials, 2001. (ISEIM 2001). Himeji, Japan 11/19/2001-11/22/2001 pp 221-224.
[22] D.V.S.S Siva Sarma and G.N.S Kalyani, "ANN approach for Condition Monitoring of Power Transformers Using DGA," (0-1803- 8560/04┬®2004 IEEE) pp 444-447.
[23] James J. Dukaram, "Transformer oil diagnosis using Fuzzy Logic and Neural Networks," Canadian Conference on Electrical & Computer Engineering, Vol. 1, pp 329-332.
[24] Jingen Wang, Lin Shang, Shifu, Chen and Yanfei Wang, "Application of Fuzzy Classification by Evolutionary Neural Network in Incipient Fault Detection of Power Transformer," Proceedings of IEEE International Joint Conference on Neural Networks, 2004, Date: 25-29 July 2004, Vol. 3, pp 2279- 2283.
[25] Diego Roberto Moaris and Jacqueline Gisete Rolim, "A Hybrid Tool for Detection of Incipient Faults in Transformers Based on Dissolved Gas Analysis of Insulating oil," IEEE Transactions on Power Delivery, Vol. 21, No. 2, April 2006, pp 673-680.
[26] M. A. Izzularab, G.E.M Aly, D.A Mansour, "On line diagnosis of Incipient Faults and Cellulose Degradation Based on Artificial Intelligence Methods," Proceedings of the 2004 IEEE International Conference on Solid Dielectrics, 2004, Vol. 2,, 7/5-9 2004 pp: 767-770.
[27] Adriana Rosa, Garcez Castro, Valdimiro Miranda, "Knowledge Discovery in Neural Networks with Application to Transformer Failure Diagnosis," IEEE Transactions on Power Systems, Vol. 20, No 2. May 2005, pp 717-724.
[28] Wei-Song Lin, Chin-Pao Hung, Mang -Hui Wang, "CMAC_ based Fault Diagnosis of Power Transformers," Proceedings of the 2002 International Joint Conference onNeural Networks 2002, Volume 3, Issue , 2002 pp 986-991.
[29] Yann-Chang Huang, "A New Data Mining Approach to Dissolved Gas Analysis of Oil-Insulated Power Apparatus," IEEE Transactions on Power Delivery, Vol 18, No. 4, October 2003, pp 1257-1261.
[30] Weigen Chen, Chong Pan, Yuxin Yun, and Yilu Liu, "Wavelet Network in Power Transformers Diagnosis Using Dissolved Gas Analysis," IEEE Transactions on Power Delivery, Vol. 24, No: 1, January 2009, pp187- 194.
[31] Karen L. Butler-Purry and Mustafa Bagriyanik, "Identifying Transformer Incipient Events for Maintaining Distribution System Reliability," Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS-03), 1/6-9/ 2003, pp-8.
[32] Hang Wang and Karen L. Butler, "Modeling Transformers with Internal Incipient Faults," IEEE Transactions on Power Systems Vol 17, No:2, April 2002, pp 500-509.
[33] S. Rajasekaran, & G.A. Vijayalakshmi Pai, "Neural Networks, Fuzzy logic and Genetic Algorithms Synthesis and Applications," Prentice-Hall of India Pvt,New Delhi. Ltd First edition, pp 297-300.