Application of the decision tree algorithm to evaluate the results of chromatographic analysis of transformer oil

DOI: 10.47026/1810-1909-2023-4-74-84

УДК 621.314

ББК 31.264-08

Anton A. DIMITRIEV, Georgi M. MIKHEEV, Huseyjon U. KALANDAROV

Key words

power transformers, diagnostics, transformer oil, operational personnel, chromatographic analysis of dissolved gases, decision tree algorithm

Abstract

Power oil-filled transformer is the main link in the process of conversion and transmission of electrical energy in electrical networks at almost all voltage classes. At the moment in our country more than 50% of them are operated with significant excess of service life, which increases the requirements to their proper technical control and full-fledged diagnostics of all its main components.

The aim of the research is to consider the ways of simplification and automation of the process of technical diagnostics of power oil-filled equipment by means of application of artificial intelligence methods, namely the decision tree algorithm for evaluation of the results of chromatographic analysis of transformer oil.

Materials and Methods. As input data, the results of chromatographic analysis of dis-solved gases in transformer oil conducted from December 11, 2009 to December 12, 2020 for two power transformers of voltage class 110 kV installed in the power system of one of the regions of our country were considered and analyzed. As a software application used for diagnosing the technical condition of a power transformer using artificial intelligence methods, we selected the free application Deductor Academic 5.3 Build 0.46.

Research results. The article considers the application of a promising method (decision tree algorithm) of interpretation of data obtained as a result of chromatographic analysis of dissolved gases in transformer oil. The analysis data were processed by means of artificial intelligence methods, the result of which was the reliability and accuracy of determining the technical condition of the power oil-filled transformer.

Conclusions. According to the results of the study, a decision tree algorithm is proposed for the implementation of artificial intelligence in solving the problem of power transformer diagnostics using chromatographic analysis results.

References

1. Alekseev B.A., Kogan F.L., Mamikonyants L.M. Ob”em i normy ispytanii elektrooborudovaniya [Scope and norms of testing of electrical equipment]. Moscow, 2003, 256 p.
2. Alekseev B.A. Kontrol’ sostoyaniya (diagnostika) krupnykh silovykh transformatorov [Condition control (diagnostics) of large power transformers]. Moscow, 2002, 216 p.
3. Batalygin P.N., Mikheev G.M., Shevtsov V.M. Inzhenernye innovatsii v regional’noi elektroenergetike [Engineering innovations in the regional electric power industry]. Elektrooborudovanie: ekspluatatsiya i remont, 2011, no. 5, pp. 36–
4. Batalygin P.N., Mikheev G.M., Shevtsov V.M. Kompleksnoe obsledovanie silovykh transformatorov [Comprehensive inspection of power transformers]. In: Kibernetika elektricheskikh sistem: sb. materialov XXVI sessii Vserossiiskogo seminara «Diagnostika energooborudovaniya» [Cybernetics of electrical systems: Proc. of the XXVI Session of the Russ. Seminar “Diagnostics of power equipment»]. Novocherkassk, 2004. pp. 14–16.
5. Eltyshev D.K. Intellektual’nye tekhnologii v organizatsii protsessa ekspluatatsii elektrotekhnicheskogo oborudovaniya [Intellectual technologies in the organization of the process of operation of electrical equipment]. Vestnik Permskogo natsional’nogo issledovatel’skogo politekhnicheskogo universiteta. Elektrotekhnika, informatsionnye tekhnologii, sistemy upravleniyaх, 2022, no. 43, pp. 119–135. DOI 10.15593/2224-9397/2022.3.07.
6. Dimitrov V.P., Borisova L.V., Khubiyan K.L. Modelirovanie znanii v zadache poiska prichin neispravnostei [Knowledge modeling in the task of searching for the causes of faults]. Inzhenernye tekhnologii i sistemy, 2021, 31, no. 3. pp. 364-379. DOI 10.15507/2658-4123.031.202103.364-379.
7. Kil’chanov S.V., Korotchenkov M.V., Shcherbatov I.A. Primenenie derev’ev reshenii pri diagnostike oborudovaniya energetiki [Application of decision trees in diagnostics of the power engineering equipment]. Informatsionnye tekhnologii. Problemy i resheniya, 2019, no. 4(9), pp. 32–36.
8. Kil’chanov P.V., Korotchenkov M.V., Shcherbatov I.A. Primenenie derev’ev reshenii pri diagnostike oborudovaniya energetiki [Application of the decision trees in diagnostics of the power engineering equipment]. Informatsionnye tekhnologii. Problemy i resheniya, 2019, no. 4(9), pp. 32–36.
9. Mikheev G.M. Transformatornoe maslo [Transformer oil]. Cheboksary, Chuvash University Publ., 2003, 156 p.
10. Mikheev G.M. Elektrostantsii i elektricheskie seti. Diagnostika i kontrol’ elektrooborudovaniya [Power stations and electrical networks. Diagnostics and control of electrical equipment]. Saratov, Profobrazovanie Publ., 2017, 297 p.
11. Platforma Loginom. Ofitsial’nyi sait kompanii BaseGroup Labs [Loginom platform. Official site of BaseGroup Labs]. Available at: URL: https://basegroup.ru/deductor/description/ (Accessed Date 2023, July 23).
12. Rutkowska D. Neural Networks, Genetic Algorithms and Fuzzy Systems. 2013. 384 с. (Russ. ed.: Neironnye seti, geneticheskie algoritmy i nechetkie sistemy. Moscow, Goryachaya liniya-Telekom Publ., 2013, 384 p.).
13. Selyutin V.E., Bodrilov P.A., Savinykh V.A. Primenenie iskusstvennogo intellekta v sfere energetiki [Application of artificial intelligence in the field of power engineering]. Moya professional’naya kar’era, 2021, vol. 1, no. 28, pp. 100–103.
14. Khal’yasmaa A.I. Mashinnoe obuchenie kak instrument povysheniya effektivnosti upravleniya zhiznennym tsiklom vysokovol’tnogo elektrooborudovaniya [Machine learning as a tool to improve the efficiency of life cycle management of high-voltage electrical equipment]. Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta, 2020, vol. 24, no. 5(154), pp. 1093–1104. DOI:21285/1814-3520-2020-5-1093-1104.
15. Lee C.T., Horng S.C. Abnormality Detection of Cast-Resin Transformers Using the Fuzzy Logic Clustering Decision Tree. , 2020, vol. 13(10), pp. 2546–2550. DOI: 10.3390/en13102546.
16. Loskutov A., Pelevin P., Vukolov V. Improving the Recognition Of Operating Modes In Intelligent Electrical Networks Based On Machine Learning Methods. E3S Web of Conferences., 2020, vol. 216, 1034–1041. DOI: 10.1051/e3sconf/202021601034.

Information about the author

Anton A. Dimitriev – Post-Graduate Student, Department of Power Supply and Intellectual Electric Power Systems named after A.A. Fedorov, Chuvash State University, Russia, Cheboksary (Meterling21@mail.ru).

Georgi M. Mikheev – Doctor of Technical Sciences, Professor, Department of Power Supply and Intellectual Electric Power Systems named after A.A. Fedorov, Chuvash State University, Russia, Cheboksary (mikheevg@rambler.ru; ORCID: https://orcid.org/0000-0003-2208-9723).

Huseyjon U. Kalandarov – Candidate of Technical Sciences, Associate Professor, Department of Transport and Energy Systems, Cheboksary Institute (branch) of Moscow Polytechnic University, Cheboksary, Russia (huseinjon.86@mail.ru).

For citations

Dimitriev A.A., Mikheev G.M., Kalandarov H.U. APPLICATION OF THE DECISION TREE ALGORITHM TO EVALUATE THE RESULTS OF CHROMATOGRAPHIC ANALYSIS OF TRANSFORMER OIL. Vestnik Chuvashskogo universiteta, 2023, no. 4, pp. 74–84. DOI: 10.47026/1810-1909-2023-4-74-84 (in Russian).

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