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Data Mining Integration of Power Grid Companies Enterprise Asset Management

  • Oleg Protalinskiy
  • Nikita Savchenko
  • Anna KhanovaEmail author
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 260)

Abstract

The issues of integration at the level of enterprise asset management systems data (EAM-systems), ontological modeling systems and data mining on defect identification in electrical equipment of Power Grid Companies are considered. Transformation of EAM-system data will be provided by special converters at the syntactic level, and at the semantic level—by the ontological model of defects and equipment of Power Grid Companies. In order to integrate EAM data with the Data Mining module, ETL procedures (Extract, Transform, Load) are used to load information about defects and equipment of a Power Grid Company. It is proposed to use artificial neural networks and decision trees for the production processes intellectualization of Power Grid Companies. Ontology and data mining models are integrated at the level of metadata concerning defects and equipment.

Keywords

Data integration Defect Electrical equipment Data mining Ontology Neural networks Decision trees 

References

  1. 1.
    Zakharenko, S.G., Malakhova, T.F., Zakharov, S.A., Brodt, V.A., Vershinin, R.S.: Accident analysis in the power grid complex. Bull. Kuzbass State Tech. Univ. 116, 94–99 (2016)Google Scholar
  2. 2.
    Protalinsky, O.M., Protalinskaya, YuO, Protalinskaya, I.O., Shcherbatov, I.A., Kladov, O.N.: Enterprise asset management of power grid companies EAMOPTIMA. Autom. IT Energy Sect. 110, 24–26 (2018)Google Scholar
  3. 3.
    Tsurikov, G.N., Shcherbatov, I.A.: The use of industrial Internet of Things at the energy facilities. Mechatron. Autom. Robot. 2, 97–100 (2018)Google Scholar
  4. 4.
    Grigoriev, A.S., Uvaisov, S.U.: Ontological approach to the integration of mobile personnel management systems at the data level. Innov. Inf. Commun. Technol. 1, 64–66 (2016)Google Scholar
  5. 5.
    Protalinsky, O.M., Khanova, A.A., Shcherbatov, I.A., Protalinsky, I.O., Kladov, O.N., Urazaliev, N.S., Stepanov, P.V.: Ontology of maintenance management process in an electric grid company. Bull Moscow Energy Inst. 6, 110–119 (2018)Google Scholar
  6. 6.
    Massel, L.V., Massel, A.G., Vorozhtsova, T.N., Makagonova, N.N.: Ontological engineering of situational management in power sector. In the collection: Knowledge—Ontologies—Theories (ZONT-2015). RAS, SB. Sobolev Institute of Mathematics, pp. 36–43 (2015)Google Scholar
  7. 7.
    Tolk, A.: Ontology, Epistemology, and Teleology for Modeling and Simulation. 372p. Springer (2013)Google Scholar
  8. 8.
    Paklin, N.B., Oreshkov, V.I.: Business Analytics: From Data to Knowledge. 704p. SPb., St. Pete (2010)Google Scholar
  9. 9.
    Antonov, I.V., Voronov, M.V.: The method of building up the ontology of the enterprise. In: Bulletin of St. Petersburg State University of Technology and Design. Natural and Technical Sciences, vol 2, pp. 28–32 (2010)Google Scholar
  10. 10.
    Gonchar, A.D.: Comparative analysis of databases and knowledge bases (ontologies) applicable to the modeling of complex processes. Mod. Sci. Res. Innov. 37, 26 (2014)Google Scholar
  11. 11.
    Efimov, P.V., Shcherbatov, I.A.: Algorithm for identifying obvious defects in process equipment in the power sector based on the neural network model. In: News of the South-West State University. Management, Computing Technology, Informatics. Medical Instrument, vol 27, pp 32–40 (2018)Google Scholar
  12. 12.
    Miftakhova, A.A.: Application of the decision tree method for solving classification and forecasting problems. Infocommun. Technol. 1, 64–70 (2016)Google Scholar
  13. 13.
    Bobyr, M.V., Kulabuhov, S.A., Milostnaya, N.A.: Training neuro-fuzzy system based on the method of area difference. Artif. Intell. Decis. Making 4, 15–26 (2016)Google Scholar
  14. 14.
    Diamantaras, K.: Artificial Neural Networks—ICANN 2010. 20th International Conference on Artificial Neural Networks, ICANN 2010, held in Thessaloniki, Greece, 15–18 Sept 2010, 543p. Springer (2010)Google Scholar
  15. 15.
    Evdokimov, I.A., Solodovnikov, V.I., Filipkov, S.V.: Using decision trees for data mining and extracting rules from neural networks. New Inf. Technol. Autom. Syst. 15, 59–67 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Moscow Power Engineering InstituteMoscowRussia
  2. 2.Astrakhan State Technical UniversityAstrakhanRussia

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