Ensuring Cyber Security in Secondary Voltage Regulation in Multi-Agent Control Systems of Cyber-Physical Microgrids
Keywords:
cyber-physical microgrid, multi-agent system, intelligent control, cyberattacks, cyber security
Abstract
The article addresses a study of the effectiveness of distributed multi-agent systems (MAS) for managing microgrids and electric networks with renewable energy sources with an emphasis on ensuring resistance to cyber threats. The purpose of the study is to develop an efficient approach to detecting and eliminating the consequences of cyberattacks through probabilistic analysis of agent behavior and algorithms for restoring data quality during secondary voltage regulation. The methods include machine learning techniques and probabilistic models to optimize control and stabilize network operation modes. The article considers various structures for control of cyber-physical microgrids based on MAS and analyzes possible cyberattacks on MAS management. The influence of cyberattacks on the MAS topology and on the consistency of agents' actions during secondary voltage regulation is shown. An approach based on probabilistic analysis and machine learning techniques has been developed that makes it possible to timely respond to cyber threats, eliminate their consequences and follow a strategy for optimal management of cyber-physical microgrids. A strategy is proposed that increases the efficiency of intelligent management and maintains the stability of energy flows even when there are information security risks.
References
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11. Hernández-Callejo L. et al. A Multi-Agent System Architecture for Smart Grid Management and Forecasting of Energy Demand in Virtual Power Plants. – IEEE Communications Magazine, 2013, 51(1), pp. 106–113, DOI:10.1109/MCOM.2013.6400446.
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13. Morstyn T., Hredzak B., Agelidis V.G. Control Strategies for Microgrids with Distributed Energy Storage Systems: An Overview. – IEEE Transactions on Smart Grid, 2018, 9(4), pp. 3652–3666, DOI: 10.1109/TSG.2016.2637958.
14. Liu W. et al. Decentralized Multi-Agent System-Based Cooperative Frequency Control for Autonomous Microgrids with Communication Constraints. – IEEE Transactions on Sustainable Energy, 2014, 5(2), pp. 446–456, DOI: 10.1109/TSTE.2013.2293148.
15. Singh V.P., Kishor N., Samuel P. Distributed Multi-Agent System Based Load Frequency Control for Multi-Area Power System in Smart Grid. – IEEE Transactions on Industrial Electronics, 2017, 64(6), pp. 5151–5160, DOI:10.1109/TIE.2017.2668983.
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19. Li Z. et al. MAS Based Distributed Automatic Generation Control for Cyber-Physical Microgrid System. –IEEE/CAA Journal of Automatica Sinica, 2016, 3(1), pp. 78–89, DOI: 10.1109/JAS.2016.7373765.
20. Гурина Л.А. Анализ киберугроз для интеллектуальных инверторов, используемых при управлении микросетями. – Безопасные информационные технологии, 2023, с. 48–51.
21 Salehghaffari H., Khodaparastan M. Dynamic Attacks Against Inverter-Based Microgrids. – IEEE Power & Energy Society General Meeting (PESGM), 2019, DOI: 10.1109/PESGM40551.2019.8973416.
22. Liu X.-K. et al. Resilient Secondary Control and Stability Analysis for DC Microgrids Under Mixed Cyber Attacks. – IEEE Transactions on Industrial Electronics, 2024, 71(2), pp. 1938–1947, DOI: 10.1109/TIE.2023.3262893.
23. Zografopoulos I., Konstantinou C. Detection of Malicious Attacks in Autonomous Cyber-Physical Inverter-Based Microgrids. – IEEE Transactions on Industrial Informatics, 2022, 18(9), pp. 5815–5826, DOI: 10.1109/TII.2021.3132131.
24. Karimi A. et al. A Resilient Control Method Against False Data Injection Attack in DC Microgrids. – 7th International Conference on Control, Instrumentation and Automation, 2021, DOI: 10.1109/ICCIA52082.2021.9403594.
25. Zhang H. et al. Distributed Load Sharing under False Data Injection Attack in an Inverter-Based Microgrid. – IEEE Transactions on Industrial Electronics, 2018, 66(2), pp. 1543–1551, DOI:10.1109/TIE.2018.2793241.
26. Wang B. et al. Consensus-Based Secondary Frequency Control under Denial-of-Service Attacks of Distributed Generations for Microgrids. – Journal of the Franklin Institute, 2019, 358(1), DOI:10.1016/j.jfranklin.2019.01.007.
27. Choeum D., Choi D.-H. Vulnerability Assessment of Conservation Voltage Reduction to Load Redistribution Attack in Unbalanced Active Distribution Networks. – IEEE Transactions on Industrial Informatics, 2021, 17(1), pp. 473–483, DOI: 10.1109/TII.2020.2980590.
28. Abdelkhalek M., Ravikumar G., Govindarasu M. ML-Based Anomaly Detection System for DER Communication in Smart Grid. – IEEE Power & Energy Society Innovative Smart Grid Technologies Conference, 2022, DOI: 10.1109/ISGT50606.2022.9817481.
29. Liang J. et al. Research and Prospect of Cyber-Attacks Prediction Technology for New Power Systems. – IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference, 2023, pp. 638–647, DOI: 10.1109/ITNEC56291. 2023.10081983.
30. Jena S., Padhy N.P. Cyber-Secure Global Energy Equalization in DC Microgrid Clusters Under Data Manipulation Attacks. – IEEE Transactions on Industry Applications, 2023, 59(5), pp. 5488–5505, DOI: 10.1109/TIA.2023.3287969.
31. Simpson-Porco J.W. et al. Secondary Frequency and Voltage Control of Islanded Microgrids via Distributed Averaging. – IEEE Transactions on Industrial Electronics, 2015, 62(11), pp. 7025–7038, DOI: 10.1109/TIE.2015.2436879.
32. Tomin N. et al. Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning. – Energies, 2021, 14, DOI: 10.3390/en14248270.
33. Zhang K. et al. Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. – arXiv, 2018, DOI:10.48550/arXiv.1802.08757.
34. Гурина Л.А., Томин Н.В. Обнаружение и подавление последствий кибератак в мультиагентных системах управления киберфизическими микросетями. – Методические вопросы исследования надежности больших систем энергетики, 2024, вып. 75
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Работа выполнена в рамках проекта государственного задания (№ FWEU-2021-0001) программы фундаментальных исследований РФ на 2021–2030 гг.
#
1. Voropay N.I. Elektrichestvo – in Russ. (Electricity), 2020, No. 7, pp. 12–21.
2. Ilyushin P.V. Energetik – in Russ. (Energetik), 2022, 4, pp. 20–27.
3. Byk F.L., Myshkina L.S. Izvestiya vysshih uchebnyh zavedeniy. Problemy energetiki – in Russ. (News of Higher Educational Institutions. Power Industry Problems), 2022, No. 24, pp. 3–15.
4. Kulikov A.L., Zinin V.M. Intellektual'naya elektrotekhnika – in Russ. (Smart Elecrical Engineering), 2022, No. 3, pp. 49–78.
5. Podkoval'nikov S.V. Elektrichestvo – in Russ. (Electricity), 2024, No. 3, pp. 4–15.
6. Shawon M.H. et al. Multi-Agent Systems in ICT Enabled Smart Grid: A Status Update on Technology Framework and Applications. – IEEE Access, 2019, vol. 7, pp. 97959–97973, DOI: 10.1109/ACCESS.2019.2929577.
7. Wang Y. et al. A Distributed Control Scheme of Microgrids in Energy Internet Paradigm and Its Multisite Implementation. – IEEE Transactions on Industrial Informatics, 2021, 17(2), pp. 1141–1153, DOI: 10.1109/TII.2020.2976830.
8. Xie G. et al. A Distributed Consensus Protocol Based on Neighbor Selection Strategies for Multi-Agent Systems Convergence. – IEEE Access, 2019, vol. 7, pp. 132937–132949, DOI: 10.1109/ACCESS.2019.2939207.
9. Gurina L.A., Tomin N.V. Voprosy kiberbezopasnosti – in Russ. (Cybersecurity Issues), 2023, 4(56), pp. 88–97.
10. Spata M.O. et al. Agents Based Smart Grid for Optimal Energy-Dispatching and Battery-Charging Algorithms. – International Journal of Engineering and Industries, 2013, vol. 4(4).
11. Hernández-Callejo L. et al. A Multi-Agent System Architecture for Smart Grid Management and Forecasting of Energy Demand in Virtual Power Plants. – IEEE Communications Magazine, 2013, 51(1), pp. 106–113, DOI:10.1109/MCOM.2013.6400446.
12. Brazier F. et al. A Review of Multi Agent Based Decentralised Energy Management Issues. – Energy Economics and Environment Conference, 2015, DOI:10.1109/EnergyEconomics.2015.7235106.
13. Morstyn T., Hredzak B., Agelidis V.G. Control Strategies for Microgrids with Distributed Energy Storage Systems: An Overview. – IEEE Transactions on Smart Grid, 2018, 9(4), pp. 3652–3666, DOI: 10.1109/TSG.2016.2637958.
14. Liu W. et al. Decentralized Multi-Agent System-Based Cooperative Frequency Control for Autonomous Microgrids with Communication Constraints. – IEEE Transactions on Sustainable Energy, 2014, 5(2), pp. 446–456, DOI: 10.1109/TSTE.2013.2293148.
15. Singh V.P., Kishor N., Samuel P. Distributed Multi-Agent System Based Load Frequency Control for Multi-Area Power System in Smart Grid. – IEEE Transactions on Industrial Electronics, 2017, 64(6), pp. 5151–5160, DOI:10.1109/TIE.2017.2668983.
16. Nguyen T.L. et al. Agent Based Distributed Control of Islanded Microgrid – Real-Time Cyber-Physical Implementation. – IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-EUROPE), 2017, DOI: 10.1109/ISGTEurope.2017.8260275.
17. Li Q. et al. Agent-Based Decentralized Control Method for Islanded Microgrids. – IEEE Transactions on Smart Grid, 2016, 7(2), pp. 637–649, DOI:10.1109/TSG.2015.2422732.
18. Morstyn T., Hredzak B., Agelidis V.G. Cooperative Multi-Agent Control of Heterogeneous Storage Devices Distributed in a DC Microgrid. – IEEE Transactions on Power Systems, 2016, 31(4), pp. 2974–2986, DOI:10.1109/TPWRS.2015.2469725.
19. Li Z. et al. MAS Based Distributed Automatic Generation Control for Cyber-Physical Microgrid System. – IEEE/CAA Journal of Automatica Sinica, 2016, 3(1), pp. 78–89, DOI: 10.1109/JAS.2016. 7373765.
20. Gurina L.A. Bezopasnye informatsionnye tekhnologii – in Russ. (Secure Information Technology), 2023, pp. 48–51.
21. Salehghaffari H., Khodaparastan M. Dynamic Attacks Against Inverter-Based Microgrids. – IEEE Power & Energy Society General Meeting (PESGM), 2019, DOI: 10.1109/PESGM40551.2019.8973416.
22. Liu X.-K. et al. Resilient Secondary Control and Stability Analysis for DC Microgrids Under Mixed Cyber Attacks. – IEEE Transactions on Industrial Electronics, 2024, 71(2), pp. 1938–1947, DOI: 10.1109/TIE.2023.3262893.
23. Zografopoulos I., Konstantinou C. Detection of Malicious Attacks in Autonomous Cyber-Physical Inverter-Based Microgrids. – IEEE Transactions on Industrial Informatics, 2022, 18(9), pp. 5815–5826, DOI: 10.1109/TII.2021.3132131.
24. Karimi A. et al. A Resilient Control Method Against False Data Injection Attack in DC Microgrids. – 7th International Conference on Control, Instrumentation and Automation, 2021, DOI: 10.1109/ICCIA52082.2021.9403594.
25. Zhang H. et al. Distributed Load Sharing under False Data Injection Attack in an Inverter-Based Microgrid. – IEEE Transactions on Industrial Electronics, 2018, 66(2), pp. 1543–1551, DOI:10.1109/TIE.2018.2793241.
26. Wang B. et al. Consensus-Based Secondary Frequency Control under Denial-of-Service Attacks of Distributed Generations for Microgrids. – Journal of the Franklin Institute, 2019, 358(1), DOI:10.1016/j.jfranklin.2019.01.007.
27. Choeum D., Choi D.-H. Vulnerability Assessment of Conservation Voltage Reduction to Load Redistribution Attack in Unbalanced Active Distribution Networks. – IEEE Transactions on Industrial Informatics, 2021, 17(1), pp. 473–483, DOI: 10.1109/TII.2020.2980590.
28. Abdelkhalek M., Ravikumar G., Govindarasu M. ML-Based Anomaly Detection System for DER Communication in Smart
Grid. – IEEE Power & Energy Society Innovative Smart Grid Technologies Conference, 2022, DOI: 10.1109/ISGT50606.2022.9817481.
29. Liang J. et al. Research and Prospect of Cyber-Attacks Prediction Technology for New Power Systems. – IEEE 6th Information Technolo-gy, Networking, Electronic and Automation Control Conference, 2023, pp. 638–647, DOI: 10.1109/ITNEC56291.2023.10081983.
30. Jena S., Padhy N.P. Cyber-Secure Global Energy Equalization in DC Microgrid Clusters Under Data Manipulation Attacks. – IEEE Transactions on Industry Applications, 2023, 59(5), pp. 5488–5505, DOI: 10.1109/TIA.2023.3287969.
31. Simpson-Porco J.W. et al. Secondary Frequency and Voltage Control of Islanded Microgrids via Distributed Averaging. – IEEE Transactions on Industrial Electronics, 2015, 62(11), pp. 7025–7038, DOI: 10.1109/TIE.2015.2436879.
32. Tomin N. et al. Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning. – Energies, 2021, 14, DOI: 10.3390/en14248270.
33. Zhang K. et al. Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. – arXiv, 2018, DOI:10.48550/arXiv.1802.08757.
34. Gurina L.A., Tomin N.V. Metodicheskie voprosy issledovaniya nadezhnosti bol'shih sistem energetiki – in Russ. (Methodological Issues of Reliability Research of Large Energy Systems), 2024, iss. 75
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The work was carried out within the framework of the state assignment project (no. FWEU-2021-0001) of the fundamental research program of the Russian Federation for 2021–2030
2. Илюшин П.В. Системный подход к развитию и внедрению распределенной энергетики и возобновляемых источников энергии в России. – Энергетик, 2022, 4, с. 20–27.
3. Бык Ф.Л., Мышкина Л.С. Эффекты интеграции локальных интеллектуальных энергетических систем. – Известия высших учебных заведений. Проблемы энергетики, 2022, № 24, с. 3–15.
4. Куликов А.Л., Зинин В.М. Требования к информационной безопасности в электроэнергетике и их реализация в интеллектуальных устройствах цифровых подстанций. – Интеллектуальная электротехника, 2022, № 3, с. 49–78.
5. Подковальников С.В. Смена парадигмы управления электроэнергетическими системами. – Электричество, 2024, № 3, с. 4–15.
6. Shawon M.H. et al. Multi-Agent Systems in ICT Enabled Smart Grid: A Status Update on Technology Framework and Applications. – IEEE Access, 2019, vol. 7, pp. 97959–97973, DOI: 10.1109/ACCESS.2019.2929577.
7. Wang Y. et al. A Distributed Control Scheme of Microgrids in Energy Internet Paradigm and Its Multisite Implementation. – IEEE Transactions on Industrial Informatics, 2021, 17(2), pp. 1141–1153, DOI: 10.1109/TII.2020.2976830.
8. Xie G. et al. A Distributed Consensus Protocol Based on Neighbor Selection Strategies for Multi-Agent Systems Convergence. – IEEE Access, 2019, vol. 7, pp. 132937–132949, DOI: 10.1109/ACCESS.2019.2939207.
9. Гурина Л.А., Томин Н.В. Разработка комплексного подхода к обеспечению кибербезопасности взаимосвязанных информационных систем при интеллектуальном управлении сообществом микросетей. – Вопросы кибербезопасности, 2023, 4(56), с. 88–97.
10. Spata M.O. et al. Agents Based Smart Grid for Optimal Energy-Dispatching and Battery-Charging Algorithms. – International Journal of Engineering and Industries, 2013, vol. 4(4).
11. Hernández-Callejo L. et al. A Multi-Agent System Architecture for Smart Grid Management and Forecasting of Energy Demand in Virtual Power Plants. – IEEE Communications Magazine, 2013, 51(1), pp. 106–113, DOI:10.1109/MCOM.2013.6400446.
12. Brazier F. et al. A Review of Multi Agent Based Decentralised Energy Management Issues. – Energy Economics and Environment Conference, 2015, DOI:10.1109/EnergyEconomics.2015.7235106.
13. Morstyn T., Hredzak B., Agelidis V.G. Control Strategies for Microgrids with Distributed Energy Storage Systems: An Overview. – IEEE Transactions on Smart Grid, 2018, 9(4), pp. 3652–3666, DOI: 10.1109/TSG.2016.2637958.
14. Liu W. et al. Decentralized Multi-Agent System-Based Cooperative Frequency Control for Autonomous Microgrids with Communication Constraints. – IEEE Transactions on Sustainable Energy, 2014, 5(2), pp. 446–456, DOI: 10.1109/TSTE.2013.2293148.
15. Singh V.P., Kishor N., Samuel P. Distributed Multi-Agent System Based Load Frequency Control for Multi-Area Power System in Smart Grid. – IEEE Transactions on Industrial Electronics, 2017, 64(6), pp. 5151–5160, DOI:10.1109/TIE.2017.2668983.
16. Nguyen T.L. et al. Agent Based Distributed Control of Islanded Microgrid – Real-Time Cyber-Physical Implementation. – IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-EUROPE), 2017, DOI: 10.1109/ISGTEurope.2017.8260275.
17. Li Q. et al. Agent-Based Decentralized Control Method for Islanded Microgrids. – IEEE Transactions on Smart Grid, 2016, 7(2), pp. 637–649, DOI:10.1109/TSG.2015.2422732.
18. Morstyn T., Hredzak B., Agelidis V.G. Cooperative Multi-Agent Control of Heterogeneous Storage Devices Distributed in a DC Microgrid. – IEEE Transactions on Power Systems, 2016, 31(4), pp. 2974–2986, DOI:10.1109/TPWRS.2015.2469725.
19. Li Z. et al. MAS Based Distributed Automatic Generation Control for Cyber-Physical Microgrid System. –IEEE/CAA Journal of Automatica Sinica, 2016, 3(1), pp. 78–89, DOI: 10.1109/JAS.2016.7373765.
20. Гурина Л.А. Анализ киберугроз для интеллектуальных инверторов, используемых при управлении микросетями. – Безопасные информационные технологии, 2023, с. 48–51.
21 Salehghaffari H., Khodaparastan M. Dynamic Attacks Against Inverter-Based Microgrids. – IEEE Power & Energy Society General Meeting (PESGM), 2019, DOI: 10.1109/PESGM40551.2019.8973416.
22. Liu X.-K. et al. Resilient Secondary Control and Stability Analysis for DC Microgrids Under Mixed Cyber Attacks. – IEEE Transactions on Industrial Electronics, 2024, 71(2), pp. 1938–1947, DOI: 10.1109/TIE.2023.3262893.
23. Zografopoulos I., Konstantinou C. Detection of Malicious Attacks in Autonomous Cyber-Physical Inverter-Based Microgrids. – IEEE Transactions on Industrial Informatics, 2022, 18(9), pp. 5815–5826, DOI: 10.1109/TII.2021.3132131.
24. Karimi A. et al. A Resilient Control Method Against False Data Injection Attack in DC Microgrids. – 7th International Conference on Control, Instrumentation and Automation, 2021, DOI: 10.1109/ICCIA52082.2021.9403594.
25. Zhang H. et al. Distributed Load Sharing under False Data Injection Attack in an Inverter-Based Microgrid. – IEEE Transactions on Industrial Electronics, 2018, 66(2), pp. 1543–1551, DOI:10.1109/TIE.2018.2793241.
26. Wang B. et al. Consensus-Based Secondary Frequency Control under Denial-of-Service Attacks of Distributed Generations for Microgrids. – Journal of the Franklin Institute, 2019, 358(1), DOI:10.1016/j.jfranklin.2019.01.007.
27. Choeum D., Choi D.-H. Vulnerability Assessment of Conservation Voltage Reduction to Load Redistribution Attack in Unbalanced Active Distribution Networks. – IEEE Transactions on Industrial Informatics, 2021, 17(1), pp. 473–483, DOI: 10.1109/TII.2020.2980590.
28. Abdelkhalek M., Ravikumar G., Govindarasu M. ML-Based Anomaly Detection System for DER Communication in Smart Grid. – IEEE Power & Energy Society Innovative Smart Grid Technologies Conference, 2022, DOI: 10.1109/ISGT50606.2022.9817481.
29. Liang J. et al. Research and Prospect of Cyber-Attacks Prediction Technology for New Power Systems. – IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference, 2023, pp. 638–647, DOI: 10.1109/ITNEC56291. 2023.10081983.
30. Jena S., Padhy N.P. Cyber-Secure Global Energy Equalization in DC Microgrid Clusters Under Data Manipulation Attacks. – IEEE Transactions on Industry Applications, 2023, 59(5), pp. 5488–5505, DOI: 10.1109/TIA.2023.3287969.
31. Simpson-Porco J.W. et al. Secondary Frequency and Voltage Control of Islanded Microgrids via Distributed Averaging. – IEEE Transactions on Industrial Electronics, 2015, 62(11), pp. 7025–7038, DOI: 10.1109/TIE.2015.2436879.
32. Tomin N. et al. Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning. – Energies, 2021, 14, DOI: 10.3390/en14248270.
33. Zhang K. et al. Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. – arXiv, 2018, DOI:10.48550/arXiv.1802.08757.
34. Гурина Л.А., Томин Н.В. Обнаружение и подавление последствий кибератак в мультиагентных системах управления киберфизическими микросетями. – Методические вопросы исследования надежности больших систем энергетики, 2024, вып. 75
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Работа выполнена в рамках проекта государственного задания (№ FWEU-2021-0001) программы фундаментальных исследований РФ на 2021–2030 гг.
#
1. Voropay N.I. Elektrichestvo – in Russ. (Electricity), 2020, No. 7, pp. 12–21.
2. Ilyushin P.V. Energetik – in Russ. (Energetik), 2022, 4, pp. 20–27.
3. Byk F.L., Myshkina L.S. Izvestiya vysshih uchebnyh zavedeniy. Problemy energetiki – in Russ. (News of Higher Educational Institutions. Power Industry Problems), 2022, No. 24, pp. 3–15.
4. Kulikov A.L., Zinin V.M. Intellektual'naya elektrotekhnika – in Russ. (Smart Elecrical Engineering), 2022, No. 3, pp. 49–78.
5. Podkoval'nikov S.V. Elektrichestvo – in Russ. (Electricity), 2024, No. 3, pp. 4–15.
6. Shawon M.H. et al. Multi-Agent Systems in ICT Enabled Smart Grid: A Status Update on Technology Framework and Applications. – IEEE Access, 2019, vol. 7, pp. 97959–97973, DOI: 10.1109/ACCESS.2019.2929577.
7. Wang Y. et al. A Distributed Control Scheme of Microgrids in Energy Internet Paradigm and Its Multisite Implementation. – IEEE Transactions on Industrial Informatics, 2021, 17(2), pp. 1141–1153, DOI: 10.1109/TII.2020.2976830.
8. Xie G. et al. A Distributed Consensus Protocol Based on Neighbor Selection Strategies for Multi-Agent Systems Convergence. – IEEE Access, 2019, vol. 7, pp. 132937–132949, DOI: 10.1109/ACCESS.2019.2939207.
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The work was carried out within the framework of the state assignment project (no. FWEU-2021-0001) of the fundamental research program of the Russian Federation for 2021–2030
Published
2024-08-29
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