Health Monitoring of Power Transformers by Dissolved Gas Analysis using Regression Method and Study the Effect of Filtration on Oil
Economically transformers constitute one of the largest investments in a Power system. For this reason, transformer condition assessment and management is a high priority task. If a transformer fails, it would have a significant negative impact on revenue and service reliability. Monitoring the state of health of power transformers has traditionally been carried out using laboratory Dissolved Gas Analysis (DGA) tests performed at periodic intervals on the oil sample, collected from the transformers. DGA of transformer oil is the single best indicator of a transformer-s overall condition and is a universal practice today, which started somewhere in the 1960s. Failure can occur in a transformer due to different reasons. Some failures can be limited or prevented by maintenance. Oil filtration is one of the methods to remove the dissolve gases and prevent the deterioration of the oil. In this paper we analysis the DGA data by regression method and predict the gas concentration in the oil in the future. We bring about a comparative study of different traditional methods of regression and the errors generated out of their predictions. With the help of these data we can deduce the health of the transformer by finding the type of fault if it has occurred or will occur in future. Additional in this paper effect of filtration on the transformer health is highlight by calculating the probability of failure of a transformer with and without oil filtrating.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328766Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2498
 R.R.Rogers, "IEEE and IEC codes to interpret faults in transformers using gas in oil analysis," IEEE Trans. On electrical Insulation, vol 13, no.5,pp. 349-354,1978.
 N.A. Muhamad, B.T. Phung, T.R. Blackburn, K.X. Lai. "Comparative study and Analysis of DGA Method for Transformer Mineral oil." IEEE Transactions on Power Tech, 2007.
 Dr. D.V.S.S. Siva Sarma and G.N.S. Kalyani. "Application of AI techniques for non-destructive evaluation of power transformer using DGA." International journal of Innovations in Energy System and power, vol 2, no1, June2007.
 Fredi Jakob, Karl Jakob, Simon Jones. "Use of Gas concentration ratio to interpret LTC & OCB dissolved gas data." Electrical Insulation Conference and Electrical Manufacturing & Coil Winding Technology Conference,2003.
 Arshad, M.; Islam, S.M. "Power transformer condition assessment using oil UV - spectrophotometry" .Electrical Insulation and Dielectric Phenomena, 2007. CEIDP 2007. Annual Report - Conference on Issue , 14-17 Oct. 2007 Page(s):611 - 614
 M.Ali, A.M.Emsley, H.Herman & R.J.Heywood. "Spectroscopic studies of the aging of cellulose paper." Polmer.vol. 42, issue 7, pp. 2893-2900.
 Abdolall, K.; Vandermaar, A.J. " Feasibility of free radical detection for condition assessment of oil/paper insulation of transformers". Conference Record of the 2008 IEEE International Symposium on Electrical Insulation, 2008. Volume , Issue , 9-12 June 2008 Page(s):182 - 186.
 Periodic oil filtration technologies. The applied Research institute for prospective Technologies.
 Michael Panik, Regression Modeling: Methods, Theory, and Computation with SAS. CRC press.
 T.O.Rouse, "Mineral Insulating oil in transformer". IEEE Electrical Insulation Magazine 1998, pp 6-16.
 Sayed A. Ward. "Evaluating transformer condition using DGA oil analysis." Electrical Insulation and Dielectric Phenomena, 2003. Annual Report Conference.
 Limin Zhang, Zheng Li, Hongzhong Ma, P.Ju. "Power transformer fault diagnosis on extension theory. Electrical Machines and Systems, 2005." ICEMS 2005. Proceedings of the Eighth International conference, 2005, Volume: 3, On page(s): 1763-1766.