ANALYSIS AND DATA PROCESSING SYSTEMS

Distributed generation (DG) plants located in close proximity to consumers are widely used in modern power industry. These plants can operate in an isolated (island) mode for a dedicated load and in parallel with the electric power system (EPS). For reliable operation of the DG plants, it is necessary to solve a number of technological problems, including the problem of optimal control of the DG plants when switching to the island mode and connecting backup diesel generator plants (DiesGP). This problem can be solved using modern intelligent control technologies. The article describes a model of a power supply system (PSS) with a powerful asynchronous load as well as a turbo-generator plant (TGP) and a DiesGP. The results of modeling the transition to an island mode with the connection of backup DiesGP are presented when the communication with the supplying EPS is interrupted. The simulation was performed in the MATLAB environment using Simulink and SimPowerSystems packages.

Based on the simulation results, the following conclusions are formulated: during the transition to the island mode, voltage dips and a decrease in frequency may occur. When connecting and synchronizing the diesel generator plant, the voltage and frequency stabilize. The use of prognostic algorithms in the regulators of the turbo-generator can significantly reduce the inertia of the object, reduce the overshoot and fluctuations in voltage and frequency when the supply EPS is disconnected. When high-voltage motors are connected to a TGP operating in the island mode, the use of an automatic prognostic controller can significantly improve the damping properties and reduce the inertia of the object; at the same time start-up of electric motors is performed more smoothly.

1. Barker Ph.P., Mello R.W. de. Determining the impact of distributed generation on power systems. Part 1. Radial distribution systems. 2000 IEEE PES Summer Meeting, Seattle, WA, USA, July 11–15, pp. 222–233.

2. Voropai N.I., Stychinsky Z.A. Renewable energy sources: theoretical foundations, technologies, technical characteristics, economics. Magdeburg, Otto-von-Guericke-Universität, 2010. 223 p.

3. Mahmoud M.S., AL-Sunni F.M. Control and optimization of distributed generation systems, Cham, Springer International Publishing, 2015. 578 p.

4. Suslov K., Solonina N., Stepanov V. A principle of power quality control in the intelligent distribution networks. International symposium on smart electric distribution systems and technologies EDST 2015: proceedings, Vienna, Austria, 2015, pp. 260–264.

5. Bulatov Yu.N., Kryukov A.V., Chan Z.Kh. Setevye klastery v sistemakh elektrosnabzheniya zheleznykh dorog [Network clusters in systems of electrical power supply of the railroads]. Irkutsk, IrGUPS Publ., 2015. 205 p.

6. Martínez Ceseña E.A., Capuder T., Mancarella P. Flexible distributed multienergy generation system expansion planning under uncertainty. IEEE Transaction on Smart Grid, 2016, vol. 7, pp. 348–357.

7. Kryukov A.V., Chan Z.Kh. Vliyanie ustanovok raspredelennoi generatsii na kachestvo elektroenergii v sistemakh elektrosnabzheniya zheleznykh dorog [Influence of installations of the distributed generation on quality of the electric power in systems of electrical power supply of the railroads]. Sovremennye tekhnologii. Sistemnyi analiz. Modelirovanie – Modern Technologies. System analysis. Modeling, 2012, no. 4 (36), pp. 162–167.

8. Kryukov A.V., Chan Z.Kh. Analiz simmetriruyushchego effekta raspredelennoi generatsii [The analysis of the symmetrizing effect of the distributed generation]. Transportnaya infrastruktura Sibirskogo regiona [Transport infrastructure of the Siberian region]. Irkutsk, 2012, vol. 2, pp. 75–81.

9. Rugthaicharoencheep N., Auchariyamet S. Technical and economic impacts of distributed generation on distribution system. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 2012, vol. 6, no. 4, pp. 385–389.

10. Arsent'ev M.O., Arsent'ev O.V., Kryukov A.V. Sistemy elektrosnabzheniya zheleznodorozhnogo transporta s ustanovkami raspredelennoi generatsii [Systems of power supply of railway transport with installations of the distributed generation]. Irkutsk, IrGTU Publ., 2013. 152 p.

11. Bulatov Yu.N., Kryukov A.V., Chan Zyui Khyng. Nechetkie regulyatory dlya vetrogeneriruyushchikh ustanovok [Indistinct regulators for the wind generator of installations]. Izvestiya vysshikh uchebnykh zavedenii. Problemy energetiki – Proceedings of the higher educational institutions. Energy sector problems, 2014, no. 7–8, pp. 60–69.

12. Voropai N.I., Etingov P.V. Two-stage adaptive fuzzy PSS application to power systems. Proceedings of International Conference on Electrical Engineering ICEE'2001, July 22–26, Xi'an, China, 2001, vol. 1, pp. 314–318.

13. Wang J., Huang A.Q., Sung W., Liu Y., Baliga B.J. Smart grid technologies. IEEE Industrial Electronics Magazine, 2009, vol. 3, no. 2, pp. 16–23.

14. Jaen-Cuellar A.Y., Romero-Troncoso R. de J., Morales-Velazquez L., Osornio-Rios R.A. PID-controller tuning optimization with genetic algorithms in servo systems. International Journal of Advanced Robotic Systems, 2013, vol. 10, pp. 324–337.

15. Bulatov Yu.N., Kryukov A.V. Optimization of automatic regulator settings of the distributed generation plants on the basis of genetic algorithm. 2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM 2016), Chelyabinsk, Russia, 2016, pp. 1–6.

16. Kryukov A.V., Kargapol'cev S.K., Bulatov Yu.N., Skrypnik O.N., Kuznetsov B.F. Intelligent control of the regulators adjustment of the distributed generation installation. Far East Journal of Electronics and Communications, 2017, vol. 17, no. 5, pp. 1127–1140.

17. Chen Y., Ma Y., Yun W. Application of improved genetic algorithm in PID controller parameters optimization. Telkomnika, 2013, vol. 11, no. 3, pp. 1524–1530.

18. Bulatov Yu.N., Kryukov A.V., Suslov K.V. Multi-agent technologies for control of distributed generation plants in the isolated power systems. Far East Journal of Electronics and Communications, 2017, vol. 17, no. 5, pp. 1197–1212.

19. Buchholz B.M., Styczynski Z.A. Smart grids: fundamentals and technologies in electricity networks. Berlin, Heidelberg, Springer-Verlag, 2014. 396 p.

20. Bulatov Yu.N., Kryukov A.V. Neuro fuzzy control system for distributed generation plants. Advances in Intelligent Systems Research, 2018, vol. 158, pp. 13–19.

21. Wang R., Wang P., Xiao G. Intelligent microgrid management and EV control under uncertainties in smart grid. Singapore, Springer, 2018. 218 p.

22. Camacho E.F., Bordons C. Model predictive control. 2nd ed. London, Springer, 2007. 405 p.

23. Pikina G.A. Printsip upravleniya po prognozu i vozmozhnost' nastroiki sistem re-gulirovaniya odnim parametrom [Principle of management according to the forecast and a possibility of control of systems of regulation by one parameter]. Novoe v rossiiskoi elektroenergetike – New in the Russian Electrical Power-Engineering, 2014, no. 3, pp. 5–13.

24. Bulatov Yu.N., Kryukov A.V., Nguen V.Kh. Prognosticheskie regulyatory dlya ustanovok raspredelennoi generatsii [Prognostic regulators for installations of the distributed generation]. Sistemy. Metody. Tekhnologii – Systems. Methods. Technologies, 2016, no. 1 (29), pp. 63–69. doi: 10.18324/2077-5415-2016-1-63-69.

25. Bulatov Yu.N., Kryukov A.V., Nguen V.Kh. Avtoprognosticheskii regulyator chastoty vrashcheniya rotora generatora ustanovki raspredelennoi generatsii [Distributed generator rotor speed automatic prognostic controller]. Nauchnyi vestnik Novosibirskogo gosudarstvennogo tekhnicheskogo universiteta – Science bulletin of the Novosibirsk state technical university, 2017, no. 1 (66), pp. 15–25.

26. SimPowerSystems: user’s guide for use with Simulink. Hydro-Que?bec, Mathworks (Firme). Natick, MA, MathWorks, 1998–2006.

27. IEEE Std. 421.5–1992. Recommended practice for excitation system models for power system stability studies. Energy Development and Power Generating Committee. IEEE, 1992.