Analytical method of identifying the type of defect of oil-filled equipment according to the results of analysis of gases dissolved in oil

Authors

  • O.V. Shutenko National Technical University «Kharkiv Polytechnic Institute», Ukraine
  • O.S. Kulyk National Technical University «Kharkiv Polytechnic Institute», Ukraine

DOI:

https://doi.org/10.15588/1607-6761-2023-2-5

Keywords:

equipment diagnostics, dissolved gas analysis (DGA), analytical method, fault type recognition, gas ratio, analysis of recognition reliability

Abstract

Purpose. Development of a method for recognizing the type of defect of oil-filled equipment based on the results of the analysis of gases dissolved in oil.

Methodology. Analysis of gas ratio values in oil-filled equipment with various types of defects, synthesis of a method for recognizing the type of defects.       

Findings. A description of the analytical method for recognising the type of defects in oil-filled equipment based on the results of the dissolved gases analysis is given. To recognise the type of defect, the values of three ratios are used: CH4/H2, C2H4/C2H6, and C2H2/C2H4. Using these ratios, 40 different types of defects and their combinations can be recognised. These defects  correspond to 25 different ranges of gas ratios obtained as a result of gas content studies for 3715 units of oil-filled equipment The type of defect is determined by analysing the obtained gas ratio values and classifying them according to the ranges of gas ratios for each fault. In the case when the obtained ratio values correspond to several types of faults in the same range, characteristic nomograms of defects and recommendations according to the key gas method are used to clarify the type of fault. A comparative analysis of the reliability of fault type recognition using the developed method and some well-known methods for interpreting the results of dissolved in oil gases analysis was performed.

Originality.  An analytical method for recognising the type of faults in oil-filled equipment of electrical networks based on the results of the dissolved gases analysis is proposed. This method differs from the existing ones in that, when using three known gas ratios, it allows recognising a larger number of defects of different types (40), including those for which the known methods do not allow establishing a diagnosis. This result is ensured by the use of 25 ranges of gas ratios obtained from the results of gas content studies for 3715 units of oil-filled equipment.

Practical value. The use of the developed method for recognising the type of faults in oil-filled equipment of electrical networks allows increasing the reliability of defect recognition based on the results of dissolved gases analysis. This, in turn, makes it possible to increase the operational reliability of electric power equipment and extend the service life of this equipment.

Author Biographies

O.V. Shutenko, National Technical University «Kharkiv Polytechnic Institute»

канд. техн. наук, доцент, доцент кафедри передачі електричної енергії Національного технічного університету «Харківський політехнічний інститут», Харків

O.S. Kulyk, National Technical University «Kharkiv Polytechnic Institute»

PhD Student of the electric power transmission department of the National Technical University “Kharkiv Polytechnic Institute”, Kharkiv

References

Diahnostyka maslonapovnenoho transformatornoho obladnannia za rezultatamy khromatohrafichnoho analizu vilnykh haziv, vidibranykh iz hazovoho rele, I haziv, rozchynenykh u izoliatsiinomu masli. Metodychni vkazivky [Diagnosis of oil-filled trans-former equipment by chromatographic analysis of free gases sampled from the gas relay and gases dis-solved in the insulating oil. Methodological guide-lines] (SOU-N EE 46.501:2006). (2007). Ministry of Fuel and Energy of Ukraine. (in Ukrainian)

International Electrotechnical Commission. (2015). Mineral oil-filled electrical equipment in ser-vice – Guidance on the interpretation of dissolved and free gases analysis (IEC 60599:2015).

Dörnenburg, E., & Strittmater, W. (1974). Monitoring oil-cooled transformers by gas analysis. Brown Boveri Review, 61, 238–274.

Rogers, R. (1978). IEEE and IEC codes to interpret incipient faults in transformers, using gas in oil analy-sis. IEEE Transactions on Electrical Insulation, EI-13(5), 349–354. https://doi.org/10.1109/tei.1978.298141

Müller, R., Schliesing, H., & Soldner, K. (1977). Die Beurteilung des Betriebszustandes von Transforma-toren durch Gasanalyse. Elektrizitätswirtschaft, (76), 345–349.

Gouda, O. E., El-Hoshy, S. H., & E.L.-Tamaly, H. H. (2018). Proposed three ratios technique for the inter-pretation of mineral oil transformers based dissolved gas analysis. IET Generation, Transmission & Distribution, 12(11), 2650–2661. https://doi.org/10.1049/iet-gtd.2017.1927

Electric Technology Research Association. (1980). Conservation and control of oil-insulated compo-nents by diagnosis of gas in oil. 36(1).

Kawamura, T., Kawada, H., Ando, K., Yamaoka, M., Maeda, T., & Takatsu, T. (1986). Analyzing gases dissolved in oil and its application to maintenance of transformers. In International Conference on Large High Voltage Electric Systems.

Lee, S.-j., Kim, Y.-m., Seo, H.-d., Jung, J.-r., Yang, H.-j., & Duval, M. (2013). New methods of DGA diagnosis using IEC TC 10 and related databases Part 2: Ap-plication of relative content of fault gases. IEEE Transactions on Dielectrics and Electrical Insula-tion, 20(2), 691–696. https://doi.org/10.1109/tdei.2013.6508774

Duval, M. (2008). The duval triangle for load tap changers, non-mineral oils and low temperature faults in transformers. IEEE Electrical Insulation Magazine, 24(6), 22–29. https://doi.org/10.1109/mei.2008.4665347

Duval, M., & Lamarre, L. (2014). The duval penta-gon-a new complementary tool for the interpretation of dissolved gas analysis in transformers. IEEE Electrical Insulation Magazine, 30(6), 9–12. https://doi.org/10.1109/mei.2014.6943428

Mansour, D.-E. A. (2015). Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases. IEEE Transactions on Dielectrics and Electri-cal Insulation, 22(5), 2507–2512. https://doi.org/10.1109/tdei.2015.004999

Emara, M. M., Peppas, G. D., & Gonos, I. F. (2021). Two graphical shapes based on DGA for power transformer fault types discrimination. IEEE Trans-actions on Dielectrics and Electrical Insulation, 28(3), 981–987. https://doi.org/10.1109/tdei.2021.009415

Gouda, O. E., El-Hoshy, S. H., & E.L.-Tamaly, H. H. (2019). Condition assessment of power transformers based on dissolved gas analysis. IET Generation, Transmission & Distribution, 13(12), 2299–2310. https://doi.org/10.1049/iet-gtd.2018.6168

Ahmed, M. R., Geliel, M. A., & Khalil, A. (n.d.). Power transformer fault diagnosis using fuzzy logic tech-nique based on dissolved gas analysis. In 2013 21st Mediterranean Conference on Control & Automa-tion (MED) (p. 584–589). https://doi.org/10.1109/med.2013.6608781

Zhang, L., & Yuan, J. (2014). Fault diagnosis of pow-er transformers using kernel based extreme learning machine with particle swarm optimization. Applied Mathematics & Information Sciences (AMIS), 9(2), 1003–1010.

Chen, Y., Yan, S., Pang, T., & Chen, R. Detection of DGA domains based on support vector machine. In 2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC). https://doi.org/10.1109/ssic.2018.8556788

Fan, J., Wang, F., Sun, Q., Bin, F., Liang, F., & Xiao, X. (2017). Hybrid RVM–ANFIS algorithm for transformer fault diagnosis. IET Generation, Trans-mission & Distribution, 11(14), 3637–3643. https://doi.org/10.1049/iet-gtd.2017.0547

Duval, M. (2002). A review of faults detectable by gas-in-oil analysis in transformers. IEEE Electrical Insulation Magazine, 18(3), 8–17. https://doi.org/10.1109/mei.2002.1014963

Muhamad, N. A., & Ali, S. A. M. (2008). LabVIEW with fuzzy logic controller simulation panel for condi-tion monitoring of oil and dry type transform-er. World Academy of Science, Engineering and Technology International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 2(8), 1685–1691. https://doi.org/10.5281/zenodo.1060253

Islam, M. M., Lee, G., & Hettiwatte, S. N. (2018). Application of Parzen Window estimation for incipi-ent fault diagnosis in power transformers. High Voltage, 3(4), 303–309. https://doi.org/10.1049/hve.2018.5061

Zeng, B., Guo, J., Zhu, W., Xiao, Z., Yuan, F., & Huang, S. (2019). A transformer fault diagnosis model based on hybrid grey wolf optimizer and LS-SVM. Energies, 12(21), Article 4170. https://doi.org/10.3390/en12214170

Nemeth, B., Laboncz, S., & Kiss, I. (n.d.). Condition monitoring of power transformers using DGA and Fuzzy logic. In 2009 IEEE Electrical Insulation Con-ference (EIC) (Formerly EIC/EME) (p. 373–376). https://doi.org/10.1109/eic.2009.5166373

Bhalla, D., Bansal, R. K., & Gupta, H. O. (2013). In-tegrating AI based DGA fault diagnosis using Demp-ster–Shafer Theory. International Journal of Electri-cal Power & Energy Systems, 48, 31–38. https://doi.org/10.1016/j.ijepes.2012.11.018

Taha, I. B. M., Hoballah, A., & Ghoneim, S. S. M. (2020). Optimal ratio limits of rogers' four-ratios and IEC 60599 code methods using particle swarm opti-mization fuzzy-logic approach. IEEE Transactions on Dielectrics and Electrical Insulation, 27(1), 222–230. https://doi.org/10.1109/tdei.2019.008395

Shutenko, O., & Kulyk, O. Diagnosis of oil-filled equipment with x-wax deposition based on dissolved gas analysis. In 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering (UKRCON) (p. 1–6). https://doi.org/10.1109/ukrcon53503.2021.9575623

Kulyk, O. S., & Shutenko, O. V. (2019). Analysis of gas content in oil-filled equipment with spark dis-charges and discharges with high energy densi-ty. Transactions on Electrical and Electronic Materi-als, 20(5), 437–447. https://doi.org/10.1007/s42341-019-00124-8

Shutenko, O., & Kulyk, O. (2022). Recognition of combined defects with high-temperature overheating based on the dissolved gas analysis. Sādhanā, 47(3), Article 146. https://doi.org/10.1007/s12046-022-01919-x

Shutenko, O., & Kulyk, O. (2020). Analysis of gas content in oil-filled equipment with low energy densi-ty discharges. International Journal on Electrical En-gineering and Informatics, 12(2), 258–277. https://doi.org/10.15676/ijeei.2020.12.2.6

Shutenko, O., & Kulyk, O. Recognition of overheat-ing with temperatures of 150-300°C by analysis of dissolved gases in oil. In 2020 IEEE 4th International Conference on Intelligent Energy and Power Systems (IEPS) (p. 71–76). https://doi.org/10.1109/ieps51250.2020.9263145

Shutenko, O., & Kulyk, O. (2022). Recognition of low-temperature overheating in power transformers by dissolved gas analysis. Electrical Engineering, 104(4), 2109–2121. https://doi.org/10.1007/s00202-021-01465-5

Shutenko, O., & Kulyk, O. Recognition of mid-temperature overheating in high-voltage power trans-formers by dissolved gas analysis. In 2021 IEEE 2nd KhPI Week on Advanced Technology (KhPIWeek) (p. 401–406). https://doi.org/10.1109/khpiweek53812.2021.9570059

Shutenko, O., & Kulyk, O. Recognition of High-Temperature Overheating in High-Voltage Power Transformers by Dissolved Gas Analysis. In 2021 IEEE International Conference on Modern Electrical and Energy Systems (MEES) (p. 1–6). https://doi.org/10.1109/mees52427.2021.9598575

Published

2023-09-15

How to Cite

Shutenko, O., & Kulyk, O. (2023). Analytical method of identifying the type of defect of oil-filled equipment according to the results of analysis of gases dissolved in oil. Electrical Engineering and Power Engineering, (2), 43–59. https://doi.org/10.15588/1607-6761-2023-2-5