Open Access
Issue
E3S Web of Conf.
Volume 365, 2023
IV International Scientific Conference “Construction Mechanics, Hydraulics and Water Resources Engineering” (CONMECHYDRO - 2022)
Article Number 04031
Number of page(s) 14
Section Mechanization, Electrification of Agriculture and Renewable Energy Sources
DOI https://doi.org/10.1051/e3sconf/202336504031
Published online 30 January 2023
  1. Barinov, V.A., Gamm, A.Z., Kucherov, Yu.N. and other. Automation of dispatch control in the electric power industry. Moscow Power Engineering Institute Publishing House, Moscow, 2000. [Google Scholar]
  2. Goldenberg, F.D. New technologies in the dispatching control of the power system of Israel. In the collection. “Energy systems management – new technologies and the market”, Syktyvkar 2004, – pp.123–130. [Google Scholar]
  3. Dyakov, A.F., Lyubarsky, Yu.Ya., Ornov, V.G., Semenov, V.A., Tsvetkov, E.V. Intelligent systems for operational management in power associations, Moscow Power Engineering Institute Publishing House, Moscow, MEI Publishing House, 1995, -236 p. [Google Scholar]
  4. Lyubarsky, Yu.Ya., Morzhin, Yu.I. The concept of “intelligent” operational information systems for automated control systems for energy systems, Publishing house “Agro-Print”, Moscow, 2002, -pp.16–22. [Google Scholar]
  5. Vasiliev, V.I., Ilyasov, B.G. Intelligent control systems: theory and practice, Radiotehnika, Moscow, 2019, 392 p. [Google Scholar]
  6. Mutushev, D.M. “Methods for providing access to object-oriented databases based on relational systems standards,” Ph.D. thesis, Moscow Power Engineering Institute, 1998. [Google Scholar]
  7. Liu, C. C., & Pierce, D. A. (1997). Intelligent System Applications to Power Systems. IEEE Computer Applications in Power, 10(4), 21–22. https://doi.org/10.1109/67.625369. [CrossRef] [Google Scholar]
  8. Lee, K. Y. (n.d.). Tutorial on Intelligent Optimization and Control for Power Systems: An Introduction. Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, 2–5. https://doi.org/10.1109/ISAP.2005.1599235. [Google Scholar]
  9. Vale, Z. A. (2009). Intelligent Power System. In Wiley Encyclopedia of Computer Science and Engineering. John Wiley & Sons, Inc. https://doi.org/10.1002/9780470050118.ecse196. [Google Scholar]
  10. Vale, Z. A., Morais, H., & Khodr, H. (2010). Intelligent multi-player smart grid management considering distributed energy resources and demand response. IEEE PES General Meeting, 1–7. https://doi.org/10.1109/PES.2010.5590170. [Google Scholar]
  11. Ishankhodjayev, G. Q., Sultanov, M. B., & Nurmamedov, B. B. (2022). Issues of development of intelligent information electric power systems. Modern Innovations, Systems and Technologies, 2(2), 0251–0263. https://doi.org/10.47813/2782-2818-2022-2-2-0251-0263. [Google Scholar]
  12. Ishankhodjayev, G., Sultanov, M., Sultanov, D., & Mirzaahmedov, D. (2021). Development of an algorithm for optimizing energy-saving management processes in intelligent energy systems. International Conference on Information Science and Communications Technologies: Applications, Trends and Opportunities, ICISCT 2021. https://doi.org/10.1109/ICISCT52966.2021.9670247. [Google Scholar]
  13. Ishankhodjayev, G., Sultanov, M., Mirzaahmedov, D., & Azimov, D. (2021). Optimization of Information Processes of Multilevel Intelligent Systems. ACM International Conference Proceeding Series. https://doi.org/10.1145/3508072.3508212. [Google Scholar]
  14. Gayrat Ishankhodjayev, & Murodjon Sultanov. (2022). Creation and application of intelligent information electric power system. Problems of Energy and Resource Saving, 2, 50–64. [Google Scholar]
  15. Kraleva, R. S., Kralev, V. S., Sinyagina, N., Koprinkova-Hristova, P., & Bocheva, N. (2018). Design and Analysis of a Relational Database for Behavioral Experiments Data Processing. International Journal of Online Engineering (IJOE), 14(02), 117. https://doi.org/10.3991/ijoe.v14i02.7988. [Google Scholar]
  16. Storey, V. C. (1991). Relational database design based on the entity-relationship model. Data & Knowledge Engineering, 7(1), 47–83. https://doi.org/10.1016/0169-023X(91)90033-T. [CrossRef] [Google Scholar]
  17. Sirotyuk, V.O. “Development of models, methods and tools for analysis and synthesis of optimal database structures in automated information and control systems,” D.Sc., Moscow Power Engineering Institute, 1999. [Google Scholar]
  18. Tsalenko, M.Sh. Relational database models. Algorithms and organization of solving economic problems, M.: Statistics, 1997, [Google Scholar]
  19. Tsikritzis, D., Lochowski, F. Data Models, Finance and statistics, Moscow, 1995. [Google Scholar]
  20. Jagadish, H. V., Lakshmanan, L. V. S., & Srivastava, D. (n.d.). Hierarchical or relational? A case for a modern hierarchical data model. Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX’99) (Cat. No.PR00453), 3–10. https://doi.org/10.1109/KDEX.1999.836523. [Google Scholar]
  21. Storey, V. C., Trujillo, J. C., & Liddle, S. W. (2015). Research on conceptual modeling: Themes, topics, and introduction to the special issue. Data & Knowledge Engineering, 98, 1–7. https://doi.org/10.1016/j.datak.2015.07.002. [CrossRef] [Google Scholar]
  22. Cohen, J., & Gil, J. (2021). An entity-relationship model of the flow of waste and resources in city-regions: Improving knowledge management for the circular economy. Resources, Conservation & Recycling Advances, 12, 200058. https://doi.org/10.1016/J.RCRADV.2021.200058. [CrossRef] [Google Scholar]
  23. Lee, H. K. (1999). Semantics of recursive relationships in entity-relationship model. Information and Software Technology, 41(13), 877–886. https://doi.org/10.1016/S0950-5849(99)00045-2. [CrossRef] [Google Scholar]
  24. Tsalenko, M.Sh. Modeling semantics in databases, Science. Ch. ed. Phys.-Math. lit., Moscow, 1998, p. 288. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.