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Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2023-23-2-289-298
Methodology for the control of electric power distribution system components to ensure the quality of consumed electricity
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Article in Russian
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Abstract
For citation:
Mozokhin A.E., Shvedenko V.N. Methodology for the control of electric power distribution system components to ensure the quality of consumed electricity. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 2, pp. 289–298. doi: 10.17586/2226-1494-2023-23-2-289-298
Abstract
Electric power system is a complex organizational structure that provides working interaction for its constituent intelligent electronic devices by defining their roles, communication channels, and powers. The control system of a modern electric power system must ensure the coordination of operation of intelligent electronic devices at the technological stages of power generation, transport, distribution, and consumption. The disadvantage of existing process control systems in electric power systems is the use of a hierarchical control structure in relation to the network topology. This fact leads to conflicts of resources and processes at the stages of generation, transport, distribution, and consumption of electricity. Uncoordinated operation of control devices leads to a decrease in the efficiency of power facilities which negatively affects the quality of electricity in the power supply network. To synchronize the work of intelligent electronic devices distributed over the network, it is proposed to provide their joint work through a single information center in a digital environment. At the same time, it is proposed to control the modes of operation of the power supply network using digital twins of its components. Digital twins of electric power system objects control power quality indicators, simulate the modes of interacting devices in a digital environment, and perform control of power supply network components to ensure a rational mode of their operation. To achieve the universality and speed of the control system it is proposed to use the apparatus of fuzzy artificial neural networks, and for better prediction of power quality indicators in the network — ensembles of artificial neural networks. A methodology for controlling the quality of electricity at sections of the electricity distribution network was developed using digital twins that ensure the relationship between the monitored indicators of electricity quality and regulated values of the actuators of intelligent electronic devices.
Keywords: control system, digital twin, ensemble of artificial neural networks, electric power system, intelligent electronic devices, power quality indicators
References
References
-
Mozokhin A.E., Shvedenko V.N. Digitization development directions of national and foreign energy systems. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 4, pp. 657–672 (in Russian). https://doi.org/10.17586/2226-1494-2019-19-4-657-672.
-
Veselov F.V., Dorofeev V.V. Smart grid of Russia as a new stage of power engineering development under conditions of digital economy. Energy Policy, 2018, no. 5, pp. 43–52. (in Russian)
-
Naderi Y., Sims R., Coffele F., Xu L. Active power quality management in smart microgrids. CIRED - Open Access Proceedings Journal, 2020, vol. 2020, no. 1, pp. 262–265. https://doi.org/10.1049/oap-cired.2021.0324
-
Naderi Y., Hosseini S.H., Zadeh S.G., Mohammadi-Ivatloo B., Vasquez J.C., Guerrero J.M. An overview of power quality enhancement techniques applied to distributed generation in electrical distribution networks. Renewable and Sustainable Energy Reviews, 2018, vol. 93, pp. 201–214. https://doi.org/10.1016/j.rser.2018.05.013
-
Zhang P., Feng Q., Chen R., Wang D., Ren L. Classification and identification of power quality in distribution network. Proc. of the 5th International Conference on Power and Renewable Energy (ICPRE), 2020, pp. 533–537. https://doi.org/10.1109/ICPRE51194.2020.9233147
-
Chowdhury P.R., Sahu P.K., Essakiappan S., Manjrekar M., Schneider K., Laval S. Power quality and stability in a cluster of microgrids with coordinated power and energy management. Proc. of the 2020 IEEE Industry Applications Society Annual Meeting, 2020, pp. 1–7. https://doi.org/10.1109/IAS44978.2020.9334828
-
Jin S., Hogewood L., Fries S., Lambert J.H., Fiondella L., Strelzoff A., Boone J., Fleckner K., Linkov I. Resilience of cyber-physical systems: Role of AI, digital twins, and edge computing. IEEE Engineering Management Review, 2022, vol. 50, no. 2, pp. 195–203. https://doi.org/10.1109/EMR.2022.3172649
-
Liu T., Yu H., Yin H., Zhang Z., Sui Z., Zhu D., Gao L, Li Z. Research and application of digital twin technology in power grid development business. Proc. of the 6th Asia Conference on Power and Electrical Engineering (ACPEE), 2021, pp. 383–387. https://doi.org/10.1109/ACPEE51499.2021.9436946
-
Fu Y., Huang Y., Hou F., Li K. A brief review of digital twin in electric power industry. Proc. of the IEEE 5th International Electrical and Energy Conference (CIEEC), 2022, pp. 2314–2318. https://doi.org/10.1109/CIEEC54735.2022.9846081
-
Ardebili A.A., Longo A., Ficarella A. Digital Twins bonds society with cyber-physical Energy Systems: a literature review. Proc. of the 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), 2021, pp. 284–289. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00054
-
Steindl G., Stagl M., Kasper L., Kastner W., Hofmann R. Generic digital twin architecture for industrial energy systems. Applied Science, 2020, vol. 10, no. 24, pp. 8903. https://doi.org/10.3390/app10248903
-
Kharlamova N., Traholt C., Hashemi S. A digital twin of battery energy storage systems providing frequency regulation. Proc. of the 2022 IEEE International Systems Conference (SysCon), 2022, pp. 1–7. https://doi.org/10.1109/SysCon53536.2022.9773919
-
Liu J., Wang S., Lu X., Li T. Research on online status evaluation technology for main equipment of power transmission and transformation based on digital twin. Proc. of the IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), 2021, pp. 3368–3373. https://doi.org/10.1109/EI252483.2021.9713501
-
Bonetti A., Harispuru C., Pitzer M., Pustejovsky M., Wetterstrand N., Kachelrieß S. Digital twin technology for virtual testing of power system relay protection. Proc. of the 3rd Global Power, Energy and Communication Conference (GPECOM), 2021, pp. 154–160. https://doi.org/10.1109/GPECOM52585.2021.9587869
-
Shvedenko V.N., Mozokhin A.E. Concept of digital twins at life cycle stages of production systems. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 6, pp. 815–827 (in Russian). https://doi.org/10.17586/2226-1494-2020-20-6-815-827
-
Shvedenko V.N., Mozokhin A.E. The concept of managing the network structure of intelligent devices in the digital transformation of the energy industry. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 5, pp. 748–754 (in Russian). https://doi.org/10.17586/2226-1494-2021-21-5-748-754
-
Ahmed M.S., Mohamed A., Shareef H., Homod R.Z., Ali J.A. Artificial neural network based controller for home energy management considering demand response events. Proc. of the International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES), 2016, pp. 506–509. https://doi.org/10.1109/icaees.2016.7888097
-
Işık E., Inalli M. Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey. Energy, 2018, vol. 154, pp. 7–16. https://doi.org/10.1016/j.energy.2018.04.069
-
Abdelaziz Y., Ali E.S. Cuckoo search algorithm based load frequency controller design for nonlinear interconnected power system. International Journal of Electrical Power & Energy Systems, 2015, vol. 73, pp. 632–643. https://doi.org/10.1016/j.ijepes.2015.05.050
-
Howell S.K., Wicaksono H., Yuce B., McGlinn K., Rezgui Y. User centered neuro-fuzzy energy management through semantic-based optimization. IEEE Transactions on Cybernetics, 2019, vol. 49, no. 9, pp. 3278–3292.
-
Labinsky A.Y., Nefedyev S.A., Bardulin E.N. The use of fuzzy logic and neural networks in automatic control systems. Bulletin of St. Petersburg University of State Fire Service of the Ministry of Emergency Situations of Russia, 2019, no. 1. Available at: https://cyberleninka.ru/article/n/ispolzovanie-nechetkoy-logiki-i-neyronnyh-setey-v-sistemah-avtomaticheskogo-upravleniya (accessed: 18.12.2022). (in Russian)
-
Parvin K., Hossain Lipu M.S., Hannan M.A., Abdullah M.A., Jern K.P., Begum R.A., Mansur M., Muttaqi K.M., Indra Mahlia T.M., Dong Z.Y. Intelligent controllers and optimization algorithms for building energy management towards achieving sustainable development: challenges and prospects. IEEE Access, 2021, vol. 9, pp. 41577–41602. https://doi.org/10.1109/ACCESS.2021.3065087
-
Kochkin V.I., Nechaev O.P. Application of Static Reactive Power Compensators in Power Grids of Power Systems and Enterprises. Moscow, NTs ENAS Publ., 2000, 248 p. (in Russian)
-
Kabyshev A.V. Reactive Power Compensation in Electrical Installations of Industrial Enterprises. Tomsk, Tomsk Polytechnic University, 2012, 234 p. (in Russian)
-
Yong Z., Li-Juan Y., Qian Z., Xiao-Yan S. Multi-objective optimization of building energy performance using a particle swarm optimizer with less control parameters. Journal of Building Engineering, 2020, vol. 32, pp. 101505. https://doi.org/10.1016/j.jobe.2020.101505