Обзор и перспективы применения алгоритмов машинного обучения для противоаварийного управления электрическими режимами энергосистем
Ключевые слова:
электроэнергетическая система, противоаварийное управление, управляющее воздействие, машинное обучение, мета-анализ
Аннотация
Трансформация современных электроэнергетических систем (ЭЭС) повышает требования к противоаварийному управлению, его быстродействию и адаптивности. Новые физические явления, обусловленные внедрением в ЭЭС возобновляемых источников энергии и систем управления на базе элементов силовой электроники, изменение принципов функционирования распределительных сетей и увеличение скорости протекания переходных процессов приводят к снижению эффективности традиционных принципов противоаварийного управления, основанных на детерминированных подходах к анализу электрических режимов. Для удовлетворения требований современных ЭЭС широкое применение находят недетерминированные методы противоаварийного управления на основе алгоритмов машинного обучения. Развитие математического аппарата и вычислительной техники делают возможным применение данного класса алгоритмов для решения задачи противоаварийного управления в режиме реального времени и разработки концепции ситуативного управления при протекании переходного процесса. В статье проведен анализ российских и зарубежных публикаций, посвященных разработке методов централизованного и локального противоаварийного управления ЭЭС на основе алгоритмов машинного обучения. Отмечены особенности рассмотренных методов и определены направления для будущих исследований.
Литература
1. Кощеев Л.А., Шульгинов Н.Г. ЦСПА на базе алгоритмов нового поколения-очередной этап в развитии противоаварийного управления в энергосистемах. – Известия НТЦ Единой энергетической системы, 2013, № 1, с. 7–14.
2. Воропай Н.И. и др. Разработка инновационных технологий и средств для оценки и повышения гибкости современных энергосистем. – Электроэнергия. Передача и распределение, 2021, № 1, с. 52–63.
3. Суворов А.А. Механизмы возникновения субсинхронных колебаний в энергосистемах с силовыми инверторными преобразователями. Ч. 1. – Электричество, 2025, № 1, c. 17–31.
4. Воропай Н.И., Ефимов Д.Н., Решетов В.И. Анализ механизма развития системных аварий в электроэнергетических системах. – Электричество, 2008, № 10, c. 12–24.
5. Бондаренко А.Ф., Говорун М.Н., Сацук Е.И. Об аварии в энергосистеме Бразилии 15 августа 2023 г. – Электрические станции, 2024, № 4, c. 14–21.
6. Сенюк М.Д., Паздерин А.В., Классен В.В. Совершенствование централизованной системы противоаварийной автоматики электроэнергетической системы на основе методов машинного обучения. – Электротехнические системы и комплексы, 2024, № 4, c.14–24.
7. Богатырев Л.Л. Распознавание аварийных ситуаций в электроэнергетических системах. – Электричество, 1978, № 6, с. 9–14.
8. Богатырев Л.Л., Богданова Л.Ф., Стихин Г.П. Выбор информативных параметров для управления режимами энергосистемы. – Электричество, 1978, № 4, с. 13–19.
9. Богатырев Л.Л., Стихин Г.П. Использование методов теории распознавания образов для. управления режимами сложных энергетических систем. – Электричество, 1978, № 12, с. 6–11.
10. Богатырев Л.Л. и др. Совершенствование алгоритмов работы систем противоаварийной автоматики. – Обмен опытом эксплуатации устройств релейной защиты и автоматики в энергосистемах Урала, 1983, с. 74–76.
11. Паздерин А. В. и др. Обзор методов идентификации места возмущения в электрических сетях на основе синхронизированных векторных измерений и алгоритмов машинного обучения. – Электричество, 2025, № 2, с. 15–28.
12. Исаев Е. В. и др. Алгоритм оценки статической устойчивости и выбора управляющих воздействий по условию обеспечения статической устойчивости в послеаварийном режиме. – Известия НТЦ Единой энергетической системы, 2013, № 1, с. 48–57.
13. Сенюк М.Д., Дмитриева А.А., Дмитриев С.А. Исследование характеристик метода экспресс-оценки параметров электрического режима в стационарных и динамических процессах. – Электротехнические системы и комплексы, 2021, № 4 (53), c. 4–12.
14. Tomin N. et al. Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning. – Energies, 2021, vol. 14, DOI: 10.3390/en14248270.
15. Negnevitsky M. et al. Development of an Intelligent System for Preventing Large-Scale Emergencies in Power Systems. – IEEE Power & Energy Society General Meeting, 2013, DOI: 10.1109/PESMG.2013.6672099.
16. Tomin N. et al. Random Forest Based Model for Preventing Large-Scale Emergencies in Power Systems. – International Journal of Artificial Intelligence, 2015, vol. 13, No. 1, pp. 211–228.
17. Tomin N., Zhukov A., Domyshev A. Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources. – EPJ Web of Conferences, 2019, vol. 217, DOI: 10.1051/epjconf/201921701016.
18. Fishov A. et al. Decentralized Emergency Control of AC Power Grid Modes with Distributed Generation. – Energies, 2023, vol. 16, No. 15, DOI: 10.3390/en16155607.
19. Марченко А.И., Фишов А.Г., Мурашкина И.С. Противоаварийная автоматика для создания и управления режимами локальных интеллектуальных энергосистем на базе малой генерации. – Альтернативная энергетика и экология, 2024, № 1, c. 225–234.
20. Huang Q. et al. Adaptive Power System Emergency Control Using Deep Reinforcement Learning. – IEEE Transactions on Smart Grid, 2020, vol. 11, No. 2, pp. 1171–1182, DOI: 10.1109/TSG.2019.2933191.
21. Vu T. et al. Safe Reinforcement Learning for Emergency Load Shedding of Power Systems. – 2021 IEEE Power & Energy Society General Meeting (PESGM), 2021, DOI: 10.1109/PESGM46819.2021.9638007.
22. Huang R. et al. Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning. – IEEE Transactions on Power Systems, 2022, vol. 37, No. 6, pp. 4168–4178, DOI: 10.1109/TPWRS.2022.3155117.
23. Huang R. et al. Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control. – arXiv, 2020, DOI: 10.48550/arXiv.2006.12667.
24. Pei Y. et al. An Emergency Control Strategy for Undervoltage Load Shedding of Power System: A Graph Deep Reinforcement Learning Method. – IET Generation, Transmission & Distribution, 2023, vol. 17, No. 9, pp. 2130–2141, DOI: 10.1049/gtd2.12795.
25. Elliott T. et al. Real Power Modulation Strategies for Transient Stability Control. – IEEE Access, 2022, vol. 10, DOI: 10.1109/ACCESS.2022.3163736.
26. Zeng H. et al. Distributed Deep Reinforcement Learning-Based Approach for Fast Preventive Control Considering Transient Stability Constraints. – CSEE Journal of Power and Energy Systems, 2021, vol. 9, No. 1, pp. 197–208, DOI: 10.17775/CSEEJPES.2020.04610.
27. Wang S. et al. Transient Stability Analysis and Emergency Generator Tripping Control Based on Spatio-Temporal Graph Deep Learning. – Energies, 2025, vol. 18, DOI: 10.3390/en18040993.
28. Wu J. et al. Adaptive Emergency Control of Power Systems Based on Deep Belief Network. – CSEE Journal of Power and Energy Systems, 2024, vol. 10, No. 4, pp. 1618–1631, DOI: 10.17775/CSEE-JPES.2022.00070.
29. Zhu L. et al. Integrated Data-Driven Power System Transient Stability Monitoring and Enhancement. – IEEE Transactions on Power Systems, 2024, vol. 39, No. 1, pp. 1797–1809, DOI: 10.1109/TPWRS.2023.3266387.
30. Zhang Z. et al. CNN-LSTM Based Power Grid Voltage Stability Emergency Control Coordination Strategy. – IET Generation, Transmission & Distribution, 2023, vol. 17, pp. 3559–3570, DOI: 10.1049/gtd2.12890.
31. Ziyue Q. et al. Emergency Control Strategy of Power System Transient Instability Based on DBN. – IOP Conference Series: Earth and Environmental Science, 2021, DOI: 10.1088/1755-1315/645/1/012016.
32. Wang C. et al. Emergency Load Shedding Strategy for Microgrids Based on Dueling Deep Q-Learning. – IEEE Access, 2021, vol. 9, DOI: 10.1109/ACCESS.2021.3055401.
33. Ren J. et al. Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset. – Energies, 2021, vol. 14, DOI: 10.3390/en14123430.
34. Zhang S. et al. A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid. – IEEE Power & Energy Society General Meeting (PESGM), 2020, DOI: 10.1109/PESGM41954.2020.9281800.
35. Chen M. et al. XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System. – IEEE Access, 2019, vol. 7, DOI: 10.1109/ACCESS.2019.2893448.
36. Kim J. et al. Transient Stability Assessment Using Deep Transfer Learning. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.3320051.
37. Chen Q. Power System Transient Stability Assessment Model Construction Using Improved SVM. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.333401.6.
38. Zhu Q. et al. A Deep End-to-End Model for Transient Stability Assessment with PMU Data. – IEEE Access, 2018, vol. 6, pp. 65474-65487, DOI: 10.1109/ACCESS.2018.2872796.
39. Chen L., Guan L. Static Information, K-Neighbor, and Self-Attention Aggregated Scheme: A Transient Stability Prediction Model with Enhanced Interpretability. – Protection and Control of Modern Power Systems, 2023, vol. 8, No. 1, DOI: 10.1186/s41601-023-00278-x.
40. Гурина Л.А., Томин Н.В. Обеспечение информационной безопасности при вторичном регулировании напряжения в мультиагентных системах управления киберфизическими микросетями. – Электричество, 2024, № 10, c. 34–45.
41. Shaimaa S. et al. A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System. Engineering. – Technology & Applied Science Research, 2024, pp. 18459–18468, DOI: 10.48084/etasr.8537.
42. Desai J., Makwana V. A Novel Out of Step Relaying Algorithm Based on Wavelet Transform and a Deep Learning Machine Model. – Protection and Control of Modern Power Systems, 2021, No. 40, DOI: 10.1186/s41601-021-00221-y.
43. Sun P. et al. Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Net-work. – arXiv, 2022, DOI: 10.48550/arXiv.2201.09245.
44. Lee H. et al. Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map. – Energies, 2023, vol. 16, DOI: 10.3390/en16237743.
45. Liu T. et al. Online Prediction and Control of Post-Fault Transient Stability Based on PMU Measurements and Multi-Task Learning. – Frontiers in Energy Research, 2023, DOI: 10.3389/fenrg. 2022.1084295.
46. Mo T. et al. Power System Transient Stability Assessment Based on Intelligent Enhanced Transient Energy Function Method. – Energies, 2024, vol. 17, DOI: 10.3390/en17235864.
47. Zhu L. et al. Structure-Aware Recurrent Learning Machine for Short-Term Voltage Trajectory Sensitivity Prediction. – IEEE Internet of Things Journal, 2024, vol. 11, No. 9, pp. 15128–15139, DOI: 10.1109/JIOT.2023.3347446.
48. Toro-Mendoza M. et al. Toward Adaptive Load Shedding Remedial Action Schemes in Modern Electrical Power Systems. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.3322657.
49. Xie J., Sun W. A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control. – IEEE Access, 2021, vol. 9, DOI: 10.1109/ACCESS.2021.3082001.
50. Dong M., Lou C., Wong C. Adaptive Under-Frequency Load Shedding. – Tsinghua Science and Technology, 2008, vol. 13, No. 6, pp. 823–828, DOI: 10.1016/S1007-0214(08)72207-7.
51. Xu B. et al. Under-Frequency Load Shedding for Power Reserve Management in Islanded Microgrids. – IEEE Transactions on Smart Grid, 2024, vol. 15, No. 5, DOI: 10.1109/TSG.2024.3393426.
52. Alavi-Koosha A., Amraee T., Oskouee S. A Multi-Area Design of Under Frequency Load Shedding Schemes Considering Energy Storage System. – Generation, Transmission & Distribution, 2023, vol. 17, pp. 4437–4452, DOI: 10.1049/gtd2.12986.
---
Исследование выполнено за счет гранта Российского научного фонда № 23-79-01024, https://rscf.ru/project/23-79-01024.
#
1. Koshcheev L.A., Shul’ginov N.G. Izvestiya NTTs Edinoy energeticheskoy sistemy – in Russ. (Proceedings of the Scientific and Technical Center of the Unified Energy System), 2013, No. 1, pp. 7–14.
2. Voropay N.I. et al. Elektroenergiya. Peredacha i raspredelenie – in Russ. (Electricity. Transmission and Distribution), 2021, No. 1, pp. 52–63.
3. Suvorov A.A. Elektrichestvo – in Russ. (Electricity), 2025, No. 1, c. 17–31.
4. Voropay N.I., Efimov D.N., Reshetov V.I. Elektrichestvo – in Russ. (Electricity), 2008, No. 10, c. 12–24.
5. Bondarenko A.F., Govorun M.N., Satsuk E.I. Elektricheskie stantsii – in Russ. (Electrical Stations), 2024, No. 4, c. 14–21.
6. Senyuk M.D., Pazderin A.V., Klassen V.V. Elektrotekhnicheskie sistemy i kompleksy – in Russ. (Electrical Engineering Systems and Complexes), 2024, No. 4, c.14–24.
7. Bogatyrev L.L. Elektrichestvo – in Russ. (Electricity), 1978, No. 6, pp. 9–14.
8. Bogatyrev L.L., Bogdanova L.F., Stihin G.P. Elektrichestvo – in Russ. (Electricity), 1978, No. 4, pp. 13–19.
9. Bogatyrev L.L., Stihin G.P. Elektrichestvo – in Russ. (Elec-tricity), 1978, No. 12, pp. 6–11.
10. Bogatyrev L.L. et al. Obmen opytom ekspluatatsii ustroystv releynoy zashchity i avtomatiki v energosistemah Urala – in Russ. (Exchange of Experience in the Operation of Relay Protection and Automation Devices in the Power Systems of the Urals), 1983, pp. 74–76.
11. Pazderin A. V. et al. Elektrichestvo – in Russ. (Electricity), 2025, No. 2, pp. 15–28.
12. Isaev E. V. et al. Izvestiya NTTs Edinoy energeticheskoy sistemy – in Russ. (Proceedings of the Scientific and Technical Center of the Unified Energy System), 2013, No. 1, pp. 48–57.
13. Senyuk M.D., Dmitrieva A.A., Dmitriev S.A. Elektrotekh-nicheskie sistemy i kompleksy – in Russ. (Electrical Engineering Systems and Complexes), 2021, No. 4 (53), c. 4–12.
14. Tomin N. et al. Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinfor-cement Learning. – Energies, 2021, vol. 14, DOI: 10.3390/en14248270.
15. Negnevitsky M. et al. Development of an Intelligent System for Preventing Large-Scale Emergencies in Power Systems. – IEEE Power & Energy Society General Meeting, 2013, DOI: 10.1109/PESMG.2013.6672099.
16. Tomin N. et al. Random Forest Based Model for Preventing Large-Scale Emergencies in Power Systems. – International Journal of Artificial Intelligence, 2015, vol. 13, No. 1, pp. 211–228.
17. Tomin N., Zhukov A., Domyshev A. Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources. – EPJ Web of Conferences, 2019, vol. 217, DOI: 10.1051/epjconf/201921701016.
18. Fishov A. et al. Decentralized Emergency Control of AC Power Grid Modes with Distributed Generation. – Energies, 2023, vol. 16, No. 15, DOI: 10.3390/en16155607.
19. Marchenko A.I., Fishov A.G., Murashkina I.S. Al’ternativnaya energetika i ekologiya – in Russ. (Alternative Energy and Ecology), 2024, No. 1, c. 225–234.
20. Huang Q. et al. Adaptive Power System Emergency Control Using Deep Reinforcement Learning. – IEEE Transactions on Smart Grid, 2020, vol. 11, No. 2, pp. 1171–1182, DOI: 10.1109/TSG.2019.2933191.
21. Vu T. et al. Safe Reinforcement Learning for Emergency Load Shedding of Power Systems. – 2021 IEEE Power & Energy Society General Meeting (PESGM), 2021, DOI: 10.1109/PESGM46819.2021 .9638007.
22. Huang R. et al. Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning. – IEEE Transactions on Power Systems, 2022, vol. 37, No. 6, pp. 4168–4178, DOI: 10.1109/TPWRS.2022.3155117.
23. Huang R. et al. Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control. – arXiv, 2020, DOI: 10.48550/arXiv.2006.12667.
24. Pei Y. et al. An Emergency Control Strategy for Undervoltage Load Shedding of Power System: A Graph Deep Reinforcement Learning Method. – IET Generation, Transmission & Distribution, 2023, vol. 17, No. 9, pp. 2130–2141, DOI: 10.1049/gtd2.12795.
25. Elliott T. et al. Real Power Modulation Strategies for Transient Stability Control. – IEEE Access, 2022, vol. 10, DOI: 10.1109/ACCESS.2022.3163736.
26. Zeng H. et al. Distributed Deep Reinforcement Learning-Based Approach for Fast Preventive Control Considering Transient Stability Constraints. – CSEE Journal of Power and Energy Systems, 2021, vol. 9, No. 1, pp. 197–208, DOI: 10.17775/CSEEJPES.2020.04610.
27. Wang S. et al. Transient Stability Analysis and Emergency Generator Tripping Control Based on Spatio-Temporal Graph Deep Learning. – Energies, 2025, vol. 18, DOI: 10.3390/en18040993.
28. Wu J. et al. Adaptive Emergency Control of Power Systems Based on Deep Belief Network. – CSEE Journal of Power and Energy Systems, 2024, vol. 10, No. 4, pp. 1618–1631, DOI: 10.17775/CSEEJPES.2022.00070.
29. Zhu L. et al. Integrated Data-Driven Power System Transient Stability Monitoring and Enhancement. – IEEE Transactions on Power Systems, 2024, vol. 39, No. 1, pp. 1797–1809, DOI: 10.1109/TPWRS.2023.3266387.
30. Zhang Z. et al. CNN-LSTM Based Power Grid Voltage Stability Emergency Control Coordination Strategy. – IET Generation, Transmission & Distribution, 2023, vol. 17, pp. 3559–3570, DOI: 10.1049/gtd2.12890.
31. Ziyue Q. et al. Emergency Control Strategy of Power System Transient Instability Based on DBN. – IOP Conference Series: Earth and Environmental Science, 2021, DOI: 10.1088/1755-1315/645/1/012016.
32. Wang C. et al. Emergency Load Shedding Strategy for Microgrids Based on Dueling Deep Q-Learning. – IEEE Access, 2021, vol. 9, DOI: 10.1109/ACCESS.2021.3055401.
33. Ren J. et al. Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset. – Energies, 2021, vol. 14, DOI: 10.3390/en14123430.
34. Zhang S. et al. A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid. – IEEE Power & Energy Society General Meeting (PESGM), 2020, DOI: 10.1109/PESGM41954.2020.9281800.
35. Chen M. et al. XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System. – IEEE Access, 2019, vol. 7, DOI: 10.1109/ACCESS. 2019.2893448.
36. Kim J. et al. Transient Stability Assessment Using Deep Transfer Learning. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.3320051.
37. Chen Q. Power System Transient Stability Assessment Model Construction Using Improved SVM. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.333401.6.
38. Zhu Q. et al. A Deep End-to-End Model for Transient Stability Assessment with PMU Data. – IEEE Access, 2018, vol. 6, pp. 65474-65487, DOI: 10.1109/ACCESS.2018.2872796.
39. Chen L., Guan L. Static Information, K-Neighbor, and Self-Attention Aggregated Scheme: A Transient Stability Prediction Model with Enhanced Interpretability. – Protection and Control of Modern Power Systems, 2023, vol. 8, No. 1, DOI: 10.1186/s41601-023-00278-x.
40. Gurina L.A., Tomin N.V. Elektrichestvo – in Russ. (Electricity), 2024, No. 10, c. 34–45.
41. Shaimaa S. et al. A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System. Engineering. – Technology & Applied Science Research, 2024, pp. 18459–18468, DOI: 10.48084/etasr.8537.
42. Desai J., Makwana V. A Novel Out of Step Relaying Algorithm Based on Wavelet Transform and a Deep Learning Machine Model. – Protection and Control of Modern Power Systems, 2021, No. 40, DOI: 10.1186/s41601-021-00221-y.
43. Sun P. et al. Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network. – arXiv, 2022, DOI: 10.48550/arXiv.2201.09245.
44. Lee H. et al. Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map. – Energies, 2023, vol. 16, DOI: 10.3390/en16237743.
45. Liu T. et al. Online Prediction and Control of Post-Fault Transient Stability Based on PMU Measurements and Multi-Task Learning. – Frontiers in Energy Research, 2023, DOI: 10.3389/fenrg.2022.1084295.
46. Mo T. et al. Power System Transient Stability Assessment Based on Intelligent Enhanced Transient Energy Function Method. – Energies, 2024, vol. 17, DOI: 10.3390/en17235864.
47. Zhu L. et al. Structure-Aware Recurrent Learning Machine for Short-Term Voltage Trajectory Sensitivity Prediction. – IEEE Internet of Things Journal, 2024, vol. 11, No. 9, pp. 15128–15139, DOI: 10.1109/JIOT.2023.3347446.
48. Toro-Mendoza M. et al. Toward Adaptive Load Shedding Remedial Action Schemes in Modern Electrical Power Systems. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.3322657.
49. Xie J., Sun W. A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control. – IEEE Access, 2021, vol. 9, DOI: 10.1109/ACCESS.2021.3082001.
50. Dong M., Lou C., Wong C. Adaptive Under-Frequency Load Shedding. – Tsinghua Science and Technology, 2008, vol. 13, No. 6, pp. 823–828, DOI: 10.1016/S1007-0214(08)72207-7.
51. Xu B. et al. Under-Frequency Load Shedding for Power Reserve Management in Islanded Microgrids. – IEEE Transactions on Smart Grid, 2024, vol. 15, No. 5, DOI: 10.1109/TSG.2024.3393426.
52. Alavi-Koosha A., Amraee T., Oskouee S. A Multi-Area Design of Under Frequency Load Shedding Schemes Considering Energy Storage System. – Generation, Transmission & Distribution, 2023, vol. 17, pp. 4437–4452, DOI: 10.1049/gtd2.12986
---
The study was financially supported by the Russian Science Foundation, grant No. 23-79-01024, https://rscf.ru/project/23-79-01024
2. Воропай Н.И. и др. Разработка инновационных технологий и средств для оценки и повышения гибкости современных энергосистем. – Электроэнергия. Передача и распределение, 2021, № 1, с. 52–63.
3. Суворов А.А. Механизмы возникновения субсинхронных колебаний в энергосистемах с силовыми инверторными преобразователями. Ч. 1. – Электричество, 2025, № 1, c. 17–31.
4. Воропай Н.И., Ефимов Д.Н., Решетов В.И. Анализ механизма развития системных аварий в электроэнергетических системах. – Электричество, 2008, № 10, c. 12–24.
5. Бондаренко А.Ф., Говорун М.Н., Сацук Е.И. Об аварии в энергосистеме Бразилии 15 августа 2023 г. – Электрические станции, 2024, № 4, c. 14–21.
6. Сенюк М.Д., Паздерин А.В., Классен В.В. Совершенствование централизованной системы противоаварийной автоматики электроэнергетической системы на основе методов машинного обучения. – Электротехнические системы и комплексы, 2024, № 4, c.14–24.
7. Богатырев Л.Л. Распознавание аварийных ситуаций в электроэнергетических системах. – Электричество, 1978, № 6, с. 9–14.
8. Богатырев Л.Л., Богданова Л.Ф., Стихин Г.П. Выбор информативных параметров для управления режимами энергосистемы. – Электричество, 1978, № 4, с. 13–19.
9. Богатырев Л.Л., Стихин Г.П. Использование методов теории распознавания образов для. управления режимами сложных энергетических систем. – Электричество, 1978, № 12, с. 6–11.
10. Богатырев Л.Л. и др. Совершенствование алгоритмов работы систем противоаварийной автоматики. – Обмен опытом эксплуатации устройств релейной защиты и автоматики в энергосистемах Урала, 1983, с. 74–76.
11. Паздерин А. В. и др. Обзор методов идентификации места возмущения в электрических сетях на основе синхронизированных векторных измерений и алгоритмов машинного обучения. – Электричество, 2025, № 2, с. 15–28.
12. Исаев Е. В. и др. Алгоритм оценки статической устойчивости и выбора управляющих воздействий по условию обеспечения статической устойчивости в послеаварийном режиме. – Известия НТЦ Единой энергетической системы, 2013, № 1, с. 48–57.
13. Сенюк М.Д., Дмитриева А.А., Дмитриев С.А. Исследование характеристик метода экспресс-оценки параметров электрического режима в стационарных и динамических процессах. – Электротехнические системы и комплексы, 2021, № 4 (53), c. 4–12.
14. Tomin N. et al. Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning. – Energies, 2021, vol. 14, DOI: 10.3390/en14248270.
15. Negnevitsky M. et al. Development of an Intelligent System for Preventing Large-Scale Emergencies in Power Systems. – IEEE Power & Energy Society General Meeting, 2013, DOI: 10.1109/PESMG.2013.6672099.
16. Tomin N. et al. Random Forest Based Model for Preventing Large-Scale Emergencies in Power Systems. – International Journal of Artificial Intelligence, 2015, vol. 13, No. 1, pp. 211–228.
17. Tomin N., Zhukov A., Domyshev A. Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources. – EPJ Web of Conferences, 2019, vol. 217, DOI: 10.1051/epjconf/201921701016.
18. Fishov A. et al. Decentralized Emergency Control of AC Power Grid Modes with Distributed Generation. – Energies, 2023, vol. 16, No. 15, DOI: 10.3390/en16155607.
19. Марченко А.И., Фишов А.Г., Мурашкина И.С. Противоаварийная автоматика для создания и управления режимами локальных интеллектуальных энергосистем на базе малой генерации. – Альтернативная энергетика и экология, 2024, № 1, c. 225–234.
20. Huang Q. et al. Adaptive Power System Emergency Control Using Deep Reinforcement Learning. – IEEE Transactions on Smart Grid, 2020, vol. 11, No. 2, pp. 1171–1182, DOI: 10.1109/TSG.2019.2933191.
21. Vu T. et al. Safe Reinforcement Learning for Emergency Load Shedding of Power Systems. – 2021 IEEE Power & Energy Society General Meeting (PESGM), 2021, DOI: 10.1109/PESGM46819.2021.9638007.
22. Huang R. et al. Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning. – IEEE Transactions on Power Systems, 2022, vol. 37, No. 6, pp. 4168–4178, DOI: 10.1109/TPWRS.2022.3155117.
23. Huang R. et al. Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control. – arXiv, 2020, DOI: 10.48550/arXiv.2006.12667.
24. Pei Y. et al. An Emergency Control Strategy for Undervoltage Load Shedding of Power System: A Graph Deep Reinforcement Learning Method. – IET Generation, Transmission & Distribution, 2023, vol. 17, No. 9, pp. 2130–2141, DOI: 10.1049/gtd2.12795.
25. Elliott T. et al. Real Power Modulation Strategies for Transient Stability Control. – IEEE Access, 2022, vol. 10, DOI: 10.1109/ACCESS.2022.3163736.
26. Zeng H. et al. Distributed Deep Reinforcement Learning-Based Approach for Fast Preventive Control Considering Transient Stability Constraints. – CSEE Journal of Power and Energy Systems, 2021, vol. 9, No. 1, pp. 197–208, DOI: 10.17775/CSEEJPES.2020.04610.
27. Wang S. et al. Transient Stability Analysis and Emergency Generator Tripping Control Based on Spatio-Temporal Graph Deep Learning. – Energies, 2025, vol. 18, DOI: 10.3390/en18040993.
28. Wu J. et al. Adaptive Emergency Control of Power Systems Based on Deep Belief Network. – CSEE Journal of Power and Energy Systems, 2024, vol. 10, No. 4, pp. 1618–1631, DOI: 10.17775/CSEE-JPES.2022.00070.
29. Zhu L. et al. Integrated Data-Driven Power System Transient Stability Monitoring and Enhancement. – IEEE Transactions on Power Systems, 2024, vol. 39, No. 1, pp. 1797–1809, DOI: 10.1109/TPWRS.2023.3266387.
30. Zhang Z. et al. CNN-LSTM Based Power Grid Voltage Stability Emergency Control Coordination Strategy. – IET Generation, Transmission & Distribution, 2023, vol. 17, pp. 3559–3570, DOI: 10.1049/gtd2.12890.
31. Ziyue Q. et al. Emergency Control Strategy of Power System Transient Instability Based on DBN. – IOP Conference Series: Earth and Environmental Science, 2021, DOI: 10.1088/1755-1315/645/1/012016.
32. Wang C. et al. Emergency Load Shedding Strategy for Microgrids Based on Dueling Deep Q-Learning. – IEEE Access, 2021, vol. 9, DOI: 10.1109/ACCESS.2021.3055401.
33. Ren J. et al. Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset. – Energies, 2021, vol. 14, DOI: 10.3390/en14123430.
34. Zhang S. et al. A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid. – IEEE Power & Energy Society General Meeting (PESGM), 2020, DOI: 10.1109/PESGM41954.2020.9281800.
35. Chen M. et al. XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System. – IEEE Access, 2019, vol. 7, DOI: 10.1109/ACCESS.2019.2893448.
36. Kim J. et al. Transient Stability Assessment Using Deep Transfer Learning. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.3320051.
37. Chen Q. Power System Transient Stability Assessment Model Construction Using Improved SVM. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.333401.6.
38. Zhu Q. et al. A Deep End-to-End Model for Transient Stability Assessment with PMU Data. – IEEE Access, 2018, vol. 6, pp. 65474-65487, DOI: 10.1109/ACCESS.2018.2872796.
39. Chen L., Guan L. Static Information, K-Neighbor, and Self-Attention Aggregated Scheme: A Transient Stability Prediction Model with Enhanced Interpretability. – Protection and Control of Modern Power Systems, 2023, vol. 8, No. 1, DOI: 10.1186/s41601-023-00278-x.
40. Гурина Л.А., Томин Н.В. Обеспечение информационной безопасности при вторичном регулировании напряжения в мультиагентных системах управления киберфизическими микросетями. – Электричество, 2024, № 10, c. 34–45.
41. Shaimaa S. et al. A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System. Engineering. – Technology & Applied Science Research, 2024, pp. 18459–18468, DOI: 10.48084/etasr.8537.
42. Desai J., Makwana V. A Novel Out of Step Relaying Algorithm Based on Wavelet Transform and a Deep Learning Machine Model. – Protection and Control of Modern Power Systems, 2021, No. 40, DOI: 10.1186/s41601-021-00221-y.
43. Sun P. et al. Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Net-work. – arXiv, 2022, DOI: 10.48550/arXiv.2201.09245.
44. Lee H. et al. Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map. – Energies, 2023, vol. 16, DOI: 10.3390/en16237743.
45. Liu T. et al. Online Prediction and Control of Post-Fault Transient Stability Based on PMU Measurements and Multi-Task Learning. – Frontiers in Energy Research, 2023, DOI: 10.3389/fenrg. 2022.1084295.
46. Mo T. et al. Power System Transient Stability Assessment Based on Intelligent Enhanced Transient Energy Function Method. – Energies, 2024, vol. 17, DOI: 10.3390/en17235864.
47. Zhu L. et al. Structure-Aware Recurrent Learning Machine for Short-Term Voltage Trajectory Sensitivity Prediction. – IEEE Internet of Things Journal, 2024, vol. 11, No. 9, pp. 15128–15139, DOI: 10.1109/JIOT.2023.3347446.
48. Toro-Mendoza M. et al. Toward Adaptive Load Shedding Remedial Action Schemes in Modern Electrical Power Systems. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.3322657.
49. Xie J., Sun W. A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control. – IEEE Access, 2021, vol. 9, DOI: 10.1109/ACCESS.2021.3082001.
50. Dong M., Lou C., Wong C. Adaptive Under-Frequency Load Shedding. – Tsinghua Science and Technology, 2008, vol. 13, No. 6, pp. 823–828, DOI: 10.1016/S1007-0214(08)72207-7.
51. Xu B. et al. Under-Frequency Load Shedding for Power Reserve Management in Islanded Microgrids. – IEEE Transactions on Smart Grid, 2024, vol. 15, No. 5, DOI: 10.1109/TSG.2024.3393426.
52. Alavi-Koosha A., Amraee T., Oskouee S. A Multi-Area Design of Under Frequency Load Shedding Schemes Considering Energy Storage System. – Generation, Transmission & Distribution, 2023, vol. 17, pp. 4437–4452, DOI: 10.1049/gtd2.12986.
---
Исследование выполнено за счет гранта Российского научного фонда № 23-79-01024, https://rscf.ru/project/23-79-01024.
#
1. Koshcheev L.A., Shul’ginov N.G. Izvestiya NTTs Edinoy energeticheskoy sistemy – in Russ. (Proceedings of the Scientific and Technical Center of the Unified Energy System), 2013, No. 1, pp. 7–14.
2. Voropay N.I. et al. Elektroenergiya. Peredacha i raspredelenie – in Russ. (Electricity. Transmission and Distribution), 2021, No. 1, pp. 52–63.
3. Suvorov A.A. Elektrichestvo – in Russ. (Electricity), 2025, No. 1, c. 17–31.
4. Voropay N.I., Efimov D.N., Reshetov V.I. Elektrichestvo – in Russ. (Electricity), 2008, No. 10, c. 12–24.
5. Bondarenko A.F., Govorun M.N., Satsuk E.I. Elektricheskie stantsii – in Russ. (Electrical Stations), 2024, No. 4, c. 14–21.
6. Senyuk M.D., Pazderin A.V., Klassen V.V. Elektrotekhnicheskie sistemy i kompleksy – in Russ. (Electrical Engineering Systems and Complexes), 2024, No. 4, c.14–24.
7. Bogatyrev L.L. Elektrichestvo – in Russ. (Electricity), 1978, No. 6, pp. 9–14.
8. Bogatyrev L.L., Bogdanova L.F., Stihin G.P. Elektrichestvo – in Russ. (Electricity), 1978, No. 4, pp. 13–19.
9. Bogatyrev L.L., Stihin G.P. Elektrichestvo – in Russ. (Elec-tricity), 1978, No. 12, pp. 6–11.
10. Bogatyrev L.L. et al. Obmen opytom ekspluatatsii ustroystv releynoy zashchity i avtomatiki v energosistemah Urala – in Russ. (Exchange of Experience in the Operation of Relay Protection and Automation Devices in the Power Systems of the Urals), 1983, pp. 74–76.
11. Pazderin A. V. et al. Elektrichestvo – in Russ. (Electricity), 2025, No. 2, pp. 15–28.
12. Isaev E. V. et al. Izvestiya NTTs Edinoy energeticheskoy sistemy – in Russ. (Proceedings of the Scientific and Technical Center of the Unified Energy System), 2013, No. 1, pp. 48–57.
13. Senyuk M.D., Dmitrieva A.A., Dmitriev S.A. Elektrotekh-nicheskie sistemy i kompleksy – in Russ. (Electrical Engineering Systems and Complexes), 2021, No. 4 (53), c. 4–12.
14. Tomin N. et al. Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinfor-cement Learning. – Energies, 2021, vol. 14, DOI: 10.3390/en14248270.
15. Negnevitsky M. et al. Development of an Intelligent System for Preventing Large-Scale Emergencies in Power Systems. – IEEE Power & Energy Society General Meeting, 2013, DOI: 10.1109/PESMG.2013.6672099.
16. Tomin N. et al. Random Forest Based Model for Preventing Large-Scale Emergencies in Power Systems. – International Journal of Artificial Intelligence, 2015, vol. 13, No. 1, pp. 211–228.
17. Tomin N., Zhukov A., Domyshev A. Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources. – EPJ Web of Conferences, 2019, vol. 217, DOI: 10.1051/epjconf/201921701016.
18. Fishov A. et al. Decentralized Emergency Control of AC Power Grid Modes with Distributed Generation. – Energies, 2023, vol. 16, No. 15, DOI: 10.3390/en16155607.
19. Marchenko A.I., Fishov A.G., Murashkina I.S. Al’ternativnaya energetika i ekologiya – in Russ. (Alternative Energy and Ecology), 2024, No. 1, c. 225–234.
20. Huang Q. et al. Adaptive Power System Emergency Control Using Deep Reinforcement Learning. – IEEE Transactions on Smart Grid, 2020, vol. 11, No. 2, pp. 1171–1182, DOI: 10.1109/TSG.2019.2933191.
21. Vu T. et al. Safe Reinforcement Learning for Emergency Load Shedding of Power Systems. – 2021 IEEE Power & Energy Society General Meeting (PESGM), 2021, DOI: 10.1109/PESGM46819.2021 .9638007.
22. Huang R. et al. Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning. – IEEE Transactions on Power Systems, 2022, vol. 37, No. 6, pp. 4168–4178, DOI: 10.1109/TPWRS.2022.3155117.
23. Huang R. et al. Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control. – arXiv, 2020, DOI: 10.48550/arXiv.2006.12667.
24. Pei Y. et al. An Emergency Control Strategy for Undervoltage Load Shedding of Power System: A Graph Deep Reinforcement Learning Method. – IET Generation, Transmission & Distribution, 2023, vol. 17, No. 9, pp. 2130–2141, DOI: 10.1049/gtd2.12795.
25. Elliott T. et al. Real Power Modulation Strategies for Transient Stability Control. – IEEE Access, 2022, vol. 10, DOI: 10.1109/ACCESS.2022.3163736.
26. Zeng H. et al. Distributed Deep Reinforcement Learning-Based Approach for Fast Preventive Control Considering Transient Stability Constraints. – CSEE Journal of Power and Energy Systems, 2021, vol. 9, No. 1, pp. 197–208, DOI: 10.17775/CSEEJPES.2020.04610.
27. Wang S. et al. Transient Stability Analysis and Emergency Generator Tripping Control Based on Spatio-Temporal Graph Deep Learning. – Energies, 2025, vol. 18, DOI: 10.3390/en18040993.
28. Wu J. et al. Adaptive Emergency Control of Power Systems Based on Deep Belief Network. – CSEE Journal of Power and Energy Systems, 2024, vol. 10, No. 4, pp. 1618–1631, DOI: 10.17775/CSEEJPES.2022.00070.
29. Zhu L. et al. Integrated Data-Driven Power System Transient Stability Monitoring and Enhancement. – IEEE Transactions on Power Systems, 2024, vol. 39, No. 1, pp. 1797–1809, DOI: 10.1109/TPWRS.2023.3266387.
30. Zhang Z. et al. CNN-LSTM Based Power Grid Voltage Stability Emergency Control Coordination Strategy. – IET Generation, Transmission & Distribution, 2023, vol. 17, pp. 3559–3570, DOI: 10.1049/gtd2.12890.
31. Ziyue Q. et al. Emergency Control Strategy of Power System Transient Instability Based on DBN. – IOP Conference Series: Earth and Environmental Science, 2021, DOI: 10.1088/1755-1315/645/1/012016.
32. Wang C. et al. Emergency Load Shedding Strategy for Microgrids Based on Dueling Deep Q-Learning. – IEEE Access, 2021, vol. 9, DOI: 10.1109/ACCESS.2021.3055401.
33. Ren J. et al. Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset. – Energies, 2021, vol. 14, DOI: 10.3390/en14123430.
34. Zhang S. et al. A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid. – IEEE Power & Energy Society General Meeting (PESGM), 2020, DOI: 10.1109/PESGM41954.2020.9281800.
35. Chen M. et al. XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System. – IEEE Access, 2019, vol. 7, DOI: 10.1109/ACCESS. 2019.2893448.
36. Kim J. et al. Transient Stability Assessment Using Deep Transfer Learning. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.3320051.
37. Chen Q. Power System Transient Stability Assessment Model Construction Using Improved SVM. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.333401.6.
38. Zhu Q. et al. A Deep End-to-End Model for Transient Stability Assessment with PMU Data. – IEEE Access, 2018, vol. 6, pp. 65474-65487, DOI: 10.1109/ACCESS.2018.2872796.
39. Chen L., Guan L. Static Information, K-Neighbor, and Self-Attention Aggregated Scheme: A Transient Stability Prediction Model with Enhanced Interpretability. – Protection and Control of Modern Power Systems, 2023, vol. 8, No. 1, DOI: 10.1186/s41601-023-00278-x.
40. Gurina L.A., Tomin N.V. Elektrichestvo – in Russ. (Electricity), 2024, No. 10, c. 34–45.
41. Shaimaa S. et al. A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System. Engineering. – Technology & Applied Science Research, 2024, pp. 18459–18468, DOI: 10.48084/etasr.8537.
42. Desai J., Makwana V. A Novel Out of Step Relaying Algorithm Based on Wavelet Transform and a Deep Learning Machine Model. – Protection and Control of Modern Power Systems, 2021, No. 40, DOI: 10.1186/s41601-021-00221-y.
43. Sun P. et al. Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network. – arXiv, 2022, DOI: 10.48550/arXiv.2201.09245.
44. Lee H. et al. Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map. – Energies, 2023, vol. 16, DOI: 10.3390/en16237743.
45. Liu T. et al. Online Prediction and Control of Post-Fault Transient Stability Based on PMU Measurements and Multi-Task Learning. – Frontiers in Energy Research, 2023, DOI: 10.3389/fenrg.2022.1084295.
46. Mo T. et al. Power System Transient Stability Assessment Based on Intelligent Enhanced Transient Energy Function Method. – Energies, 2024, vol. 17, DOI: 10.3390/en17235864.
47. Zhu L. et al. Structure-Aware Recurrent Learning Machine for Short-Term Voltage Trajectory Sensitivity Prediction. – IEEE Internet of Things Journal, 2024, vol. 11, No. 9, pp. 15128–15139, DOI: 10.1109/JIOT.2023.3347446.
48. Toro-Mendoza M. et al. Toward Adaptive Load Shedding Remedial Action Schemes in Modern Electrical Power Systems. – IEEE Access, 2023, vol. 11, DOI: 10.1109/ACCESS.2023.3322657.
49. Xie J., Sun W. A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control. – IEEE Access, 2021, vol. 9, DOI: 10.1109/ACCESS.2021.3082001.
50. Dong M., Lou C., Wong C. Adaptive Under-Frequency Load Shedding. – Tsinghua Science and Technology, 2008, vol. 13, No. 6, pp. 823–828, DOI: 10.1016/S1007-0214(08)72207-7.
51. Xu B. et al. Under-Frequency Load Shedding for Power Reserve Management in Islanded Microgrids. – IEEE Transactions on Smart Grid, 2024, vol. 15, No. 5, DOI: 10.1109/TSG.2024.3393426.
52. Alavi-Koosha A., Amraee T., Oskouee S. A Multi-Area Design of Under Frequency Load Shedding Schemes Considering Energy Storage System. – Generation, Transmission & Distribution, 2023, vol. 17, pp. 4437–4452, DOI: 10.1049/gtd2.12986
---
The study was financially supported by the Russian Science Foundation, grant No. 23-79-01024, https://rscf.ru/project/23-79-01024
