Open Access
Issue
E3S Web Conf.
Volume 289, 2021
International Conference of Young Scientists “Energy Systems Research 2021”
Article Number 07019
Number of page(s) 3
Section Power Engineering
DOI https://doi.org/10.1051/e3sconf/202128907019
Published online 13 July 2021
  1. S. Vaez-Zadeh and E. Jalali, “Combined vector control and direct torque control method for high performance induction motor drives, “ Energy conversion and management, Vol. 48, pp. 3095-3101, 2007. [Google Scholar]
  2. Casadei D, Profumo F, Serra G, Tani A (2002) FOC and DTC: two viable schemes for induction motors torque control. IEEE Trans Power Electron 17(5):779–787 [Google Scholar]
  3. Diab AAZ (2017) Implementation of a novel full-order observer for speed sensorless vector control of induction motor drives. Elect Eng 99(3):907–921. [Google Scholar]
  4. Diab AAZ, Kotin DA, Pankratov VV (2013) Speed control of sensorless induction motor drive based on model predictive control. In: Micro/Nanotechnologies and Electron Devices (EDM), 2013 14th International Conference of Young Specialists on, IEEE conference, vol., no., pp 269–274 [Google Scholar]
  5. I. Rakhmonov, A. Berdishev, N. Niyozov, A. Muratov and U. Khaliknazarov. Development of a scheme for generating the predicted value of specific electricity consumption // CONMECHYDRO – 2020. IOP Conf. Series: Materials Science and Engineering. 883 (2020) 012103. doi:10.1088/1757-899X/883/1/012103 [Google Scholar]
  6. F.A. Hoshimov, I.I. Bakhadirov, M.S. Kurbanbayeva, N.A. Aytbayev. Development of specific standards of energy consumption by types of produced products of the spinning product // RSES 2020. E3S Web of Conferences. 216 (2020) 01169. https://doi.org/10.1051/e3sconf/202021601169 [Google Scholar]
  7. F.A. Hoshimov, I.I. Bakhadirov, A.A. Alimov, M.T. Erejepov. Forecasting the electric consumption of objects using artificial neural networks // E3S Web of Conferences. 216 (2020) 01170. [EDP Sciences] [Google Scholar]
  8. I.U. Rakhmonov, F.A. Hoshimov. Development of an algorithm for evaluating the dominant factors that have the greatest impact on the energy intensity of products // ENERGY-21. E3S Web of Conferences. 209 (2020) 07018. https://doi.org/10.1051/e3sconf/202020907018 [Google Scholar]
  9. Usmanov E.G. Stability in a parallel resonant circuit with active load // RSES 2020. E3S Web of Conferences. 216 (2020) 01160. https://doi.org/10.1051/e3sconf/202021601160 [Google Scholar]
  10. Usmanov E.G., Khusanov B.M. Phase relations in resonant circuits with a wide falling section on the amplitude characteristic // RSES 2020. E3S Web of Conferences. 216 (2020) 01161. https://doi.org/10.1051/e3sconf/202021601161 [Google Scholar]
  11. I.U. Rakhmonov, K.M. Reymov and S.H. Dustova. Improvements in industrial energy rationing methods // MIP: Engineering-2020. E3S Web of Conferences. 862 (2020) 062070. doi:10.1088/1757-899X/862/6/062070 [Google Scholar]
  12. I.U. Rakhmonov, K.M. Reymov. Statistical models of renewable energy intermittency // RSES 2020. E3S Web of Conferences. 216 (2020) 01167. https://doi.org/10.1051/e3sconf/202021601167 [Google Scholar]
  13. I.U. Rakhmonov, N.N. Kurbonov. Analysis of automated software for monitoring energy consumption and efficiency of industrial enterprises // E3S Web of Conferences. 216 (2020) 01178. https://doi.org/10.1051/e3sconf/202021601178 [EDP Sciences] [Google Scholar]
  14. F.A. Hoshimov, I.U. Rakhmonov, N.N. Niyozov. Technology to reduce energy costs in the electric steel melting shop // ENERGY-21. E3S Web of Conferences. 209 (2020) 07017. https://doi.org/10.1051/e3sconf/202020907017 [Google Scholar]
  15. I. Bakhadirov, N. Markaev, G. Aslanova, R. Tanatarov, S. Makhmuthonov. Differentiated tariffs of electricity for the improvement of steelmaking Uzbekistan // CONMECHYDRO – 2020. IOP Conf. Series: Materials Science and Engineering. 883 (2020) 012121. doi:10.1088/1757-899X/883/1/012121 [Google Scholar]
  16. A.D. Taslimov. Selection of a complex of parameters of distribution electric networks with respect to technical limitations // ENERGY-21. E3S Web of Conferences. 209 (2020) 07013. https://doi.org/10.1051/e3sconf/202020907013 [Google Scholar]
  17. Olimjon Toirov, Аllabergan Bekishev, Sardor Urakov and Utkir Mirkhonov E3S Web of Conferences 216, 01116 (2020), https://doi.org/10.1051/e3sconf/202021601116 [EDP Sciences] [Google Scholar]
  18. Olimjon Toirov, Kamoliddin Alimkhodjaev, Nurali Pirmatov and Aziza Kholbutaeva E3S Web of Conferences 216, 01119 (2020), https://doi.org/10.1051/e3sconf/202021601119 [EDP Sciences] [Google Scholar]
  19. K.M. Reymov, G. Turmanova, S. Makhmuthonov, B. Uzakov. Mathematical models and algorithms of optimal load management of electrical consumers // E3S Web of Conf. 216 (2020) 01166. https://doi.org/10.1051/e3sconf/202021601166 [Google Scholar]
  20. A.N. Rasulov, M.R. Ruzinazarov, N. Toirova, T.Sh. Alibekova. Graphical-analytical method for constructing load characteristics // RSES 2020. E3S Web of Conferences. 216 (2020) 01171. https://doi.org/10.1051/e3sconf/202021601171 [Google Scholar]
  21. Yu. Bobozhonov, B. Seytmuratov, B. Fayzullaev, A. Sultonov. Study of the influence of different designs of massive rotor of asynchronous generator on their maximum power // RSES 2020. E3S Web of Conferences. 216 (2020) 01177. https://doi.org/10.1051/e3sconf/202021601177 [Google Scholar]
  22. Rakhmonov I U, Reymov K M, Najimova A M, Uzakov B T and Seytmuratov BT 2019 Analysis and calculation of optimum parameters of electric arc furnace Journal of Physics: Conference Series 1399 doi:10.1088/17426596/1399/5/055048 [Google Scholar]
  23. Yu.M. Bobozhonov, K.M. Reymov, B.T. Seytmuratov, T.Kh. Khakimov. Research of the dependence of the resistance of asynchronous generators with massive rotors on their design // RSES 2020. E3S Web of Conferences. 216 (2020) 01168. https://doi.org/10.1051/e3sconf/202021601168 [Google Scholar]

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