Fault Prediction Based on Dissolved Gas Concentration from Insulating Oil in Power Transformer Using Neural Network

Abstract:

Reliable and continued performance of power transformer is the key to profitable generation and transmission of electric power. Failure of a large power transformer not only results in the loss of expensive equipment, but it can cause significant guarantied damage as well. Replacement of that transformer can take up to a year if the failure is not disastrous and can result in tremendous revenue losses and fines. A power transformer in operation is subjected to various stresses like thermal stress and electrical stress, resulting in liberation of gases from the hydrocarbon mineral oil. Dissolved gas analysis is a technique used to assess incipient faults of the transformer by analyzing specific dissolved gas concentrations arising from the deterioration of the transformer. DGA is used not only as a diagnostic tool but also to track apparatus failure. In this research work the dissolved gas values measured for a 230kV / 110kV power transformer which are obtained from electricity board are used as references to the developed neural network. The neural network is trained and the transformer faults are predicted. The trained neural network shows the good performance for the prediction of fault in a 230kV/110kV power transformer.

• Periodical: Applied Mechanics and Materials (Volume 441)
• Pages: 312-317
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.441.312
• Online since: December 2013
• Authors: Fang Ming Yang, Chuan Liu, Yong Sun, Qi Long, You Ping Fan
• Keywords: 230/110kV Power Transformer, Dissolved Gas Analysis, Neural Network (NN)

References

[1] A. Shintemirov, W. Tang and Q. H. Wu, 'Power Transformer Fault Classification Based on Dissolved Gas Analysis by Implementing Bootstrap and Genetic Programming', IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, VOL. 39, NO. 1, p.69–79, January (2009).

DOI: 10.1109/tsmcc.2008.2007253

[2] SUN Yan-jing, ZHANG Shen, MIAO Chang-xin, LI Jing-meng, Improved BP Neural Network for Transformer Fault Diagnosis, Journal of China University of Mining & Technology, Vol. 17 No. 1, p.138–142, Mars (2007).

DOI: 10.1016/s1006-1266(07)60029-7

[3] 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 transactions on power delivery, VOL. 23, NO. 2, p.751–759, April (2008).

DOI: 10.1109/tpwrd.2008.915812

[4] Chin-Pao Hung, Mang-Hui Wang, Diagnosis of incipient faults in power transformers using CMAC neural network approach, Electric Power Systems Research 71, p.235–244, (2004).

DOI: 10.1016/j.epsr.2004.01.019

[5] Rogers R. R., IEEE and IEC codes for incipient faults in Transformers using Analysis Elec. Insu1. l3. No. 5, oct-(1978).

[6] Standard IEC 60599, 'Guide for the interpretation of dissolved gas analysis and gas-free', (2007).

[7] Michel Duval, A Review of Faults Detectable by Gas-in-Oil Analysis in Transformers, IEEE Electrical Insulation Magazine, Vol. 18, No. 3, p.8–17, June (2002).

DOI: 10.1109/mei.2002.1014963

[8] Rahmatollah Hooshmand and Mahdi Banejad, Application of Fuzzy Logic in Fault diagnosis in Transformers using Dissolved Gas based on Different Standards, World Academy of Science, Engineering and Technology 17, p.157–161, (2006).

DOI: 10.5370/jeet.2008.3.3.293

[9] Z. Moravej, D.N. Vishwakarma, S.P. Singh, 'Application of radial basis function neural network for differential relaying of a power transformer', Computers and Electrical Engineering 29, p.421–434, (2003).

DOI: 10.1016/s0045-7906(01)00033-7

[10] L. Haykin, Neural Networks: A comprehensive Foundation. Montreal, Canada: Macmillan College Publishing Company Inc., (1994).