Review of Machine Learning Algorithms and Prospects of Applying Them for Emergency Control of Power System Electrical Operation Modes
DOI:
https://doi.org/10.24160/0013-5380-2025-10-22-36Keywords:
electric power system, emergency control, control action, machine learning, meta-analysisAbstract
The transformation of modern electric power systems (EPS) entails the need to impose more stringent requirements for emergency control in terms of its response speed and adaptability. The emergence of new physical phenomena caused by the introduction in the EPS of renewable energy sources and control systems based on power electronics, changes in the principles according to which distribution networks operate, and a higher speed of transients are factors that deteriorate the efficiency of conventional emergency control principles based on deterministic approaches to the analysis of electrical operation modes. To meet the requirements of modern EPS, non-deterministic methods of emergency control based on machine learning algorithms are extensively used. The progress in development of mathematical apparatus and computer technology opens the possibility of applying this class of algorithms for addressing emergency control matters in real time and elaborating a concept of situational control at the pace of a transient. The article considers Russian and foreign publications focused on the development of methods for centralized and local emergency control of EPS based on machine learning algorithms. The features of the considered methods are emphasized, and directions for future research are outlined.
References
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.
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Исследование выполнено за счет гранта Российского научного фонда № 23-79-01024, https://rscf.ru/project/23-79-01024.
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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
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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.
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Исследование выполнено за счет гранта Российского научного фонда № 23-79-01024, https://rscf.ru/project/23-79-01024.
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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
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The study was financially supported by the Russian Science Foundation, grant No. 23-79-01024, https://rscf.ru/project/23-79-01024
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2025-08-28
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