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About one class of the problems of optimal stochastic control of hybrid dynamical systems

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EN
Abstrakty
EN
A new class of the problems of optimal stochastic control of hybrid dynamical systems different from well-known ones by the introduction of additional extreme and probabilistic constraints on the phase variables is studied in the present work. The mathematical formulation and approximate method of solution of the examined class of the problems are presented in this work. The effectiveness of the use of this class of the problems is illustrated on the example of one of the largest water main of Ukraine.
Twórcy
autor
  • Kharkiv National University of Radio Electronics
  • Kharkiv National University of Radio Electronics
Bibliografia
  • 1. Novitsky N., Sukharev M., Tevyashev A., 2015. Energy Pipeline System: Methodological and Applied Problems of Mathematical Modeling. - Novosibirsk: Nauka, 476. (in Russian).
  • 2. Evdokimov A., Teviashev A., 1980. Operational management of load flow in engineering networks. - Kharkiv: Vyscha shkola, 144. (in Russian).
  • 3. Karelin V., Minayev A., 1986. Pumps and pumping stations. M.: Stroyizdat, 320. (in Russian).
  • 4. Chupin R., Melehov E., 2011. The Theory and Practice of Modeling and Optimization of Water and Wastewater. – Irkutsk: Publisher Irkutsk State Technical University, 323. (in Russian).
  • 5. Lobachev P., 1983. Pumps and pumping stations. - Moscow: Stroyizdat, 191. (in Russian).
  • 6. Teviashev A., Matvienko O., 2014. About one Approach to Solve the Problem of Management of the Development and Operation of Centralized Water–Supply Systems. Econtechmod. An International Quarterly Journal, Vol. 3, № 3, 61–76.
  • 7. Teviashev A., Matvienko O., 2015. Mathematical Model and Method of Optimal Stochastic Control of the Modes of Operation of the Water Main. East–European Journal of Enterprise Technologies, 6/4(78), 45-53. (in Russian).
  • 8. Tevyashev A., Matviyenko O., 2015. About One Problem of Optimal Stochastic Control of the Modes of Operation of Water Mains. Econtechmod. An International Quarterly Journal, Vol. 4, № 3, 3–12.
  • 9. Tevyashev A., Nikitenko G., Matviyenko O., 2015. Optimal Stochastic Control of the Modes of Operation of the Sewage Pumping Station. Econtechmod. An International Quarterly Journal. 4, Issue 3, 47–55.
  • 10. Sotnikov D., 2014. Development of Methodology for Improving the Energy Efficiency of Pumping Stations. Journal of Vilnius Gediminas Technical University, 1, 105–106.
  • 11. Petrov D., 2005. Adjustable Drive to Pump Applications. Power Electronics, 4, 18–22.
  • 12. Pulido–Calvo I., Gutiérrez–Estrada J., 2011. Selection and operation of pumping stations of water distribution systems. Environmental Research Journal, Nova Science Publishers, 5 (3), 120.
  • 13. Leznov B., 2006. Energy saving and adjustable drive for pump and blower installations. – Moscow: Energoatomizdat, 358. (in Russian).
  • 14. Reinbold C., Hart V., 2011. The search for energy savings: optimization of existing & new pumping stations. Florida Water Resources Journal, 44–52.
  • 15. Lipták B., 2009. Pumping station optimization. Control promoting excellence in process automation, 12–19.
  • 16. Burgschweiger J., Gnadig B., Steinbach M., 2009. Optimization Models for Operative Planning in Drinking Water Networks. Optimization and Engineering. 10, №1, 43–73.
  • 17. Gulyi A., Kobyzska A., 2012. Pumping Equipment Effectiveness Increase by Means of Ejector Application as Preliminary Stage for High-Speed Pump Units. Motrol. Commission of Motorization and Energetics in Agriculture Polish Academy of Sciences. University of Engineering and Economics in Rzeszow. Lublin – Rzeszow. 14, №1, 158–164.
  • 18. Ruuskanen A., 2007. Optimization of Energy Consumption in Wastewater Pumping. Lappeenranta University of Technology Department of Energy- and Environmental Technology. 99.
  • 19. Saliev E., 2013. Reliability of the Functioning of the Water Supply and Sewerage System. Motrol. Commission of Motorization and Energetics in Agriculture Polish Academy of Sciences. University of Engineering and Economics in Rzeszow. Lublin – Rzeszow. 15, №5, 53–61.
  • 20. Steinbach M., 2001. General Information Constraints in Stochastic Programs. Konrad-Zuse- Zentrum fur Informations technik. Berlin, ZIB-Report, 5.
  • 21. Bertsekas D., Shreve S., 1985. Stochastic Optimal Control. The Discrete Time Case. Moscow: Nauka, 280. (in Russian).
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-091fe85e-2622-49e4-b211-ea49cae823c5
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