Practical Aspects of Recognition of Electric Type Defects on the Analysis Results of Gases Dissolved in Oil

Oleg V. Shutenko, Volodymyr B. Abramov, Ivan S. Yakovenko


Background. Recognition of the type of defects in high-voltage oil-filled equipment, based on the analysis of gases dissolved in oil, using the values of the criteria that are regulated by the normative document in force in Ukraine, doesn’t always allow us to establish the correct diagnosis. Given that misdiagnosis can lead to accidental damage, ways to improve the reliability of defect type recognition are considered.

Objective. The aim of the paper is to increase the reliability of recognition of the type of defects in high-voltage oil-filled equipment, based on the analysis of gases dissolved in oil using the normative document in force in Ukraine.

Methods. A comparative analysis of the values of the ratios of gases and graphical images, for 31 types of electrical defects, for 808 units of oil-filled equipment was performed. A brief description and values of the ratios of gases and graphic images for defects, the recognition of which causes difficulties with the use of the normative document in force in Ukraine, is given.

Results. It is established that in real operation conditions the values of the diagnostic criteria used to recognize the type of defects can significantly differ from the values regulated by the normative documents in force both in Ukraine and abroad. Moreover, for some defects, the values of the gas ratios simultaneously correspond to different defects, and the reference graphic images are absent, which considerably complicates the procedure for their recognition. To eliminate these drawbacks, graphical images and ranges of values of gas ratios are offered, which allow us to recognize a greater number of varieties of defects, compared to the normative document in force in Ukraine, 31 instead of 6.

Conclusions. The ranges of values of gas ratios obtained and given in the work and the graphic images of defects are a diagnostic scheme, the practical use of which makes it possible to significantly expand the range of defects recognized by the analysis of gases dissolved in oil and thereby improve the operational reliability of high-voltage oil-filled equipment.


Analysis of gases dissolved in oil; Gas ratio; Graphic images; Defect type; Partial discharges; Spark discharges; Creeping discharges; Arc; X-wax; Authenticity of recognition


B.A. Alekseev, Monitoring of the State (Diagnostics) of Large Power Transformers. Moscow, Russia: Izdatelstvo NTs ENAS, 2002.

Diagnostics of Oil-Filled Transformer Equipment Based on Chromatographic Analysis of Free Gases Selected from Gas Relay and Gases Dissolved in Insulating Oil, SOU-N EE 46.501:2006.

Interpretation of the Analysis of Gases in Transformer and Other Oil Med Electrical Equipment in Geneva, IEC Publication 60599, Switzerland, 1999.

IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers, IEEE Standard C57.104-2008, Feb. 2009.

Guidelines for the Diagnosis of Developing Defects in Transformer Equipment According to the Results of Chromatographic Analysis of Gases Dissolved in Oil, RD 153-34.0-46.302-00. Moscow: NTs ENAS, 2001.

V.B. Abramov, “Distinctive features of gas formation in transformer oil hermetic and unsealed high-voltage equipment”, Novyny Energetiki, no. 9, pp. 17–32, 2009.

V.B. Abramov, “Features of the control of oil-filled equipment according to the results of chromatographic analysis of gases dissolved in oil”, Elektricheskie Seti i Sistemyi, no. 4, pp. 77–79, 2012.

G.M. Boyarchukov, “Practical problems of assessing the state of high-voltage equipment on the content of gases in transformer oil”, Novyny Energetiki, no. 7, pp. 23–33, 2010.

O.V. Shutenko, “Analysis of the characteristics of the gas content of oils in unpressurized defect-free transformers”, Visnyk Natsionalnoho Tekhnichnoho Universytetu “Kharkivskyi Politekhnichnyi Instytut”. Ser. Tekhnika ta Elektrofizyka Vysokykh Napruh, no. 38, pp. 84–97, 2017.

M.V. Kosteriev and Ye.I. Bardyk, The Question of Constructing Fuzzy Models for Assessing the Technical Condition of Objects of Electrical Systems. Kyiv, Ukraine: NTUU KPI, 2011.

P.D. Lezhniuk et al., “Diagnosis of power transformers using fuzzy sets”, Visnyk Vinnytskoho Politekhnichnoho Instytutu, no. 1, pp. 43–51, 2005.

O.V. Shutenko, “Analysis of graphic images based on the results of HDGA for high-voltage power transformers with various types of defects”, Visnyk Natsionalnoho Tekhnichnoho Universytetu “Kharkivskyi politekhnichnyi instytut”. Ser. Enerhetyka: Nadiinist ta Enerhoefektyvnist, no. 31, pp. 97–121, 2017.

O. Shutenko and I. Jakovenko, “Fault diagnosis of power transformer using method of graphic images”, in Proc. 2017 IEEE YSF-2017, Lviv, Ukraine, Oct. 17–20, 2017, рp. 66–69. doi: 10.1109/YSF.2017.8126594

C.-P. Hung and M.-H. Wang, “Diagnosis of incipient faults in power transformers using CMAC neural network approach”, Electric Power Syst. Res., vol. 71, no. 3, pp. 235–244. 2004. doi: 10.1016/j.epsr.2004.01.019

I. Kaur and P. Singh. “Residual Life assessment with DGA, Furan content in transformer oil and Degree of polymerization of solid insulation”, Int. J. Innov. Res. Electrical, Electronics, Instrument. Control Eng., vol. 6, no. 7, July 2016, pp. 230–234.

W. Feng et al., “Research of transformer intelligent evaluation and diagnosis method based on DGA”, MATEC Web of Conferences, vol. 77, pp. 1–6, 2016. doi: 10.1051/matecconf/20167701002

M. Mehulkumar B and A. Shahpatel, “Special approach to detecting incipient fault in power transformer using dissolved gas analysis”, Int. J. Innov. Res. Electrical, Electronics, Instrument. Control Eng., vol. 3, no. 5, May 2015, pp. 186–188. doi: 10.17148/IJIREEICE.2015.3544

M. Duval, “A review of faults detectable by gas-in-oil analysis in transformers”, IEEE Electr. Insul. Mag., vol. 18, no. 3, pp. 8–17, 2002. doi: 10.1109/MEI.2002.1014963

A.Yu. Ryizhkina, “Analysis and improvement of chromatographic methods for diagnosing oil-filled high-voltage electrical equipment”, Ph.D. dissertation, Novosibirsk, Russia, 2012.

M.M.B. Yaacob et al., “DGA method-based ANFIS expert system for diagnosing faults and assessing quality of power transformer insulation oil”, Modern Appl. Sci., vol. 10, no. 1, pp 13–22, 2016. doi: 10.5539/mas.v10n1p13

A.R. Gouda and D. Patra, “Image processing based analysis of transformer oil”, Bachelor thesis, Department of Electrical Engineering, National Institute of Technology, Rourkela, Inidia, 2014, 63 p.

V.I. Komarov. (2008). On the Effect of GK Oil on the Reliability of Electrical Equipment [Online]. Available:

K. Shrivastava and A. Choubey, “A novel association rule mining with iec ratio based dissolved gas analysis for fault diagnosis of power transformers”, Int. J. Adv. Comp. Res., vol. 2, no. 2, pp. 34–44, 2012.

A.R. Hussein et al., “Ann expert system for diagnosing faults and assessing the quality insulation oil of power transformer depending on the DGA method”, J. Theor. Appl. Inform. Technol., vol. 78, no. 2, pp. 278–285, 2015.

S. Ghoneim et al., “Detection of faults in power transformers using an expertise method depending on DGA”, in Proc. 15th Int. Middle East Power Systems Conference (MEPCON’12), 2012, pp. 1–6.

U. Roland and O. Eseosa, “Artificial neural network approach to distribution transformers maintenance”, Int. J. Sci. Res. Eng. Technol., vol. 1, no. 4, pp. 62–70, 2015.

G.M. Boyarchukov, “Practical problems of assessing the state of high-voltage equipment on the content of gases in transformer oil”, Novyny Energetiki, no. 10, pp. 24–33, 2010.

Ikb al Abulmageed Hameed, “Monitoring power transformer using fuzzy logic”, J. Eng. Develop., vol. 17, no. 6, pp. 146–163, 2013.

M.-J. Lin, “A new approach with three dimension figure and ANSI/IEEE C57.104 Standard rule diagnoses transformer’s insulating oil”, Engineering, vol. 6, no. 12, рр. 841–848, 2014. doi: 10.4236/eng.2014.612078

J. Liu et al., “A comparative research on power transformer fault diagnosis based on several artificial neural networks”, J. Comput. Inform. Syst., vol. 18, pp. 7501–7508, 2013.

A.-P. Chen and C.-C. Lin, “Fuzzy approaches for fault diagnosis of transformers”, Fuzzy Sets and Systems, vol. 118, no. 1, pp. 139–151, 2001. doi: 10.1016/S0165-0114(99)00115-3

N.A. Muhamad and S.A.M. Ali, “LabVIEW with fuzzy logic controller simulation panel for condition monitoring of oil and dry type transformer”, Proc. World Academy of Science: Engineering and Technology, vol. 50, pp. 153–159, 2006.

S. Ghoneim and K.A. Shoush, “Diagnostic tool for transformer fault detection based on dissolved gas analysis”, Advances in Electrical Engineering Systems, no. 3, vol. 1, pp 152–156, 2012.

H. Ahadpour, “A novel approach for diagnosis of power transformers internal faults using an electronic nose”, J. Basic Appl. Sci. Res., vol. 1, no. 7, pp. 808–815, 2011.

S.S.M. Ghoneim and I.B. Taha, “Artificial neural networks for power transformers fault diagnosis based on iec code using dissolved gas analysis”, Int. J. Control Automat. Syst., vol. 4, no. 2, pp. 18–21, 2015.

H.A. Illias et al., “Transformer incipient fault prediction using combined artificial neural network and various particle swarm optimisation techniques”, PLOS ONE, vol. 10, no. 6, e0129363, 2015. doi: 10.1371/journal.pone.0129363

Z.B. Sahri and R.B. Yusof, “Support vector machine-based fault diagnosis of power transformer using k nearest-neighbor imputed DGA dataset”, J. Comp. Commun., vol. 2, pp. 22–31, 2014. doi: 10.4236/jcc.2014.29004

O.V. Shutenko, “Features of the dynamics of changes in the criteria used to interpret the results of HDGA in power transformers with different types of defects”, Novoe v Rossiyskoy Elektroenergetike, no. 9, pp. 30–49, 2017.

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