Обеспечение информационной безопасности при вторичном регулировании напряжения в мультиагентных системах управления киберфизическими микросетями
Ключевые слова:
киберфизическая микросеть, мультиагентная система, интеллектуальное управление, кибератаки, информационная безопасность
Аннотация
Статья посвящена исследованию эффективности распределенных мультиагентных систем (МАС) для управления микросетями и электрическими сетями с возобновляемыми источниками энергии с акцентом на обеспечение устойчивости к киберугрозам. Цель исследования – создание эффективного подхода к обнаружению и устранению последствий кибератак через вероятностный анализ поведения агентов и алгоритмы восстановления качества данных при вторичном регулировании напряжения. Методы включают машинное обучение и вероятностные модели для оптимизации управления и стабилизации режимов сетей. В статье рассмотрены различные схемы управления киберфизическими микросетями на основе МАС, проведен анализ возможных кибератак на МАС управления. Показано влияние кибератак на топологию МАС и согласованность действий агентов при вторичном регулировании напряжения. Разработан подход на основе вероятностного анализа и машинного обучения, позволяющий своевременно реагировать на киберугрозы, устранять их последствия и следовать стратегии оптимального управления киберфизическими микросетями. Предложена стратегия, повышающая эффективность интеллектуального управления и сохраняющая стабильность энергопотоков даже при рисках информационной безопасности.
Литература
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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.
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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.
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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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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.
#
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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
