Remote monitoring and control of the status of transformers in distribution electrical networks

   RELEVANCE. One of the most important expected effects of digitalization of objects of the power grid complex of the Russian Federation is to increase the level of reliability of its functioning. In this regard, research and development in this direction is undoubtedly relevant.
   THE PURPOSE. To propose a mathematical model for monitoring dangerous developing defects in power oil-filled transformers, this would meet the properties of predictability and adaptability. Based on the model, to develop a decision-making algorithm for long-term reliable operation of
transformers.

   METHODS: Methods of statistical pattern recognition theory, correlation analysis and Bayesian classification will be used to solve the problems to ensure high reliability of diagnostic assessments, validity and effectiveness of operational solutions.

   RESULTS. A predictive model was obtained and verified in the form of a correlation function of a sign of a faulty state of a transformer from the values of its electrical load. An event tree has been formed that restores the causal relationship between the result of monitoring the transformer, the defect sign and the operational decision being made. Based on the event tree and diagnostic evaluation criteria, a control algorithm is implemented, with the help of which calculations are performed confirming the effectiveness of the proposed approach.

   CONCLUSION. The possibility of effective application of the developed defect recognition model and operational control algorithm as tools of industrial technology of the Internet of Things is illustrated, in particular, when organizing remote diagnostic monitoring of oil-filled transformer equipment at substations of the distribution grid area.

1. Metody nerazrushayushchego kontrolya. Available at: https://ntcexpert.ru//953-metody-nerazrushayushchego-kontrolya. Accessed: 17 Feb. 2023.

2. Orekhov E. A., Abramov V. V. Metody nerazrushayushchego kontrolya elektrotekhnicheskogo oborudovaniya. Energoekspert. 2020; 2: 10-33.

3. Mehairjan R. P. Y, Zhuang Q., Djairam D. et al. High Voltage Technology & Asset Management. Delft University of Technology. Improved Risk Analysis Through Failure Mode Classification According to Occurrence Time. IEEE International Conference on Condition Monitoring and Diagnosis. 23-27 September 2012, Bali, Indonesia, doi: 10.1109/CMD.2012.6416287.

4. Davidenko I. V., Halikova E. D. Uchet riskov pri vybore ocherednosti meropriyatij tekhnicheskogo obsluzhivaniya silovyh transformatorov. ELEKTRO. 2014. 6. Pp. 32-37.

5. Soderholm, Р. and Norrbin, Р. (2013), Risk-based dependability approach to maintenance performance measurement. Journal of Quality in Maintenance Engineering, Vol. 19 No. 3, pp. 316-329. doi: 10.1108/JQME-05-2013-0023.

6. Ashraf W. S., Singh R. A., Shiraz S., et. al. Advances in DGA based condition monitoring of transformers: A review, Renewable and Sustainable Energy Reviews, Elsevier, 2021. vol. 149 (C). doi: 10.1016/j.rser.2021.111347.

7. Mackenzie Е. С., Crossey J., de Pablo F., et. al. On-line monitoring and diagnostics for power transformers. 2010 IEEE International Symposium on Electrical Insulation, doi: 10.1109/ELINSL.2010.5549734.

8. Cheng X., Wang Y., The remote monitoring system of transformer fault based on The internet of Things, Proceedings of 2011 International Conference on Computer Science and Network Technology, Harbin, 2011, pp. 84-87, doi: 10.1109/ICCSNT.2011.6181914.

9. Asadia F., Phumphob S., Pongswatd S. Remote monitoring and alert system of HV transformer based on FMEA. Energy Reports 6 (2020) 807-813. doi: 10.1016/j.egyr.2020.11.128.

10. Mitra A., Dutta M., Pramanick A. Digitalization of Power Transformer Monitoring System, 2022 IEEE India Council International Subsections Conference (INDISCON), Bhubaneswar, India, 2022, pp. 1-5. doi: 10.1109/INDISCON54605.2022.9862843.

11. Zaharov О. A. Cifrovizaciya elektrosetevogo kompleksa: puti resheniya ili sistema prognostiki i monitoringa, Rukovodyashchie materialy po proektirovaniyu i ekspluatacii elektricheskih setej, 2019, 3 (587). Available at: https://prana-system.com/novosti/novosti/cifrovizaciya-elektrosetevogo-kompleksa-puti-resheniya-ili-sistema-prognostiki-i-monitoringa. Accessed: 17 Feb. 2023.

12. Levin V. M., Yah'ya A. A. Ciffovye modeli prediktivnoj analitiki dlya udalennogo monitoringa transformatornogo oborudovaniya. Metodicheskie voprosy issledovaniya nadezhnosti bol'shih sistem energetiki: Nadezhnost' energosnabzheniya potrebitelej v usloviyah ih cifrovoj transformacii. - Irkutsk: ISEM SO RAN, 2021; 72 (l): 393-402.

13. Khovalova T. V., 2019. Innovations in the Electric Power Industry: Types, Classification and Effects of Implementation, Strategic decisions and risk management, Real Economy Publishing House. 2019; 3: 274-283. doi: doi: 10.17747/2618-947X-2019-3-274-283.

14. Papiya Yu. S. Ispol'zovanie tekhnologij interneta veshchej v elektroenergetike: vozmozhnosti i ogranicheniya v processe perekhoda. Nauchnye zapiski molodyh issledovatelej. 2019; 5: 56-64.

15. Mozohin A. E., SHvedenko V. N. Analiz napravlenij razvitiya ciffovizacii otechestvennyh i zarubezhnyh energeticheskih sistem. Nauchno-tekhnicheskij vestnik informacionnyh tekhnologij, mekhaniki i optiki. 2019; 19 (4): 657-672. doi: 10.17586/2226-1494-2019-19-4-657-672.

16. L'vov Yu. N. Metodologiya prinyatiya reshenij pri ocenke tekhnicheskogo sostoyaniya silovyh transformatorov i avtotransformatorov elektricheskih setej s uchyotom faktora riska povrezhdeniya. Elektricheskie stancii. 2019; 9: 14-20.

17. Levin V. M., Yah'ya A. A. Sistema informacionno-analiticheskoj podderzhki prinyatiya reshenij po ekspluatacii silovyh transformatorov. Glavnyj energetik. 2020. 9. Pp. 52-62.

18. Yah'ya A. A., Levin V. M. Bayesian Classifier is the Tool of Increasing the Efficiency of Defects Recognition in Power Transformers. Proceedings of the higher educational institutions. ENERGY SECTOR PROBLEMS. 2018; 20 (9-10): 71-78. doi: 10.30724/1998-9903-2019-21-6-11-18.

19. Yah'ya A. A., Levin V. M. Adaptac.iya prediktivnoj modeli klassifikacii defektov v transformatorah po kolichestvu i sostavu kontroliruemyh parametrov. Borisovskie chteniya: materialy 3 Vseros. nauch-tekhn. konf. s mezhdunar. uchastiem, Krasnoyarsk. SLU. 2021. Pp. 192-196.

20. Lipshtejn R. A., Shahnovich M. I. Transformatornoe maslo. M.: Energiya, 1983. 296 p.

21. Kizevetter D. V., Savina A. Yu., Zhuravleva N. M. et al. К voprosu о diagnostike sostoyaniya transformatornyh masel v processe ekspluatacii. Nauchno-tekhnicheskie vedomosti Sankt-Peterburgskogo gosudarstvennogo politekhnicheskogo universiteta. 2013; 3 (178): 118-125.

22. Senoussaoui М. Е., Fofana I., Brahami M. Influence of Oil Quality on the Interpretation of Dissolved Gas Analysis Data. 2021 IEEE 5^thth International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), Kozhikode, India. 2021. Pp. 170-175. doi: 10.1109/CATCON52335.2021.9670513.

23. Berman A. L., Pavlov N. Yu., Nikolajchuk O. A. Metod sinteza i analiza derev'ev otkazov na osnove ponyatij mekhanizma i kinetiki sobytij. Problemy analiza riska. 2018; 15 (3): 62-77.

24. Borovikov V. P. Populyarnoe vvedenie v sovremennyj analiz dannyh i mashinnoe obuchenie na STATISTICA. M.: Nauchno-tekhnicheskoe izdatel'stvo Goryachaya liniya - Telekom. 2018. 354 p.