Обзор методов идентификации места возмущения в электрических сетях на основе синхронизированных векторных измерений и алгоритмов машинного обучения
Ключевые слова:
энергосистема, короткое замыкание, синхронизированные векторные измерения, машинное обучение, мета-анализ
Аннотация
Внедрение в электрические сети возобновляемых источников электроэнергии, устройств управления на базе силовой электроники и интеллектуальных систем анализа и управления привели к изменению динамики переходных процессов в электроэнергетических системах. Увеличение скорости протекания переходных процессов, вызванное снижением суммарной инерции энергосистем, предъявляет новые требования к быстродействию устройств противоаварийной автоматики и релейной защиты. Одним из перспективных направлений, позволяющим существенно снизить алгоритмическую задержку устройств управления и защиты, является использование алгоритмов машинного обучения для синтеза закона управления и идентификации места возмущения. В статье дан анализ работ, посвящённых адаптивным методикам локализации места возмущения в распределительных и магистральных электрических сетях на основе алгоритмов машинного обучения и синхронизированных векторных измерений. Проведено сравнение исследований, посвященных алгоритмам машинного обучения, точности и вычислительной задержки. На основе проведённого мета-анализа определены перспективные направления исследований проблемы идентификации места возмущения.
Литература
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9. Коровкин Н.В., Бодренков Е.А. Аналитический расчет узловых нагрузок распределительной электрической сети. – Электричество, 2021, № 9, c. 31–37.
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11. Usman M.U., Faruque M.O. Validation of a PMU-Based Fault Location Identification Method for Smart Distribution Network with Photovoltaics Using Real-Time Data. – IET Generation Transmission Distribution, 2018, vol. 12, pp. 5824–5833, DOI: 10.1049/ietgtd.2018.6245.
12. Chen Z. et al. Time Domain Distribution System Fault Location based on Dynamic State Estimation. – IEEE Power & Energy Society General Meeting (PESGM), 2024, DOI: 10.1109/PESGM51994.2024.10688815.
13. Pang Q. et al. Multi-Timescale-Based Fault Section Location in Distribution Networks. – IEEE Access, 2021, vol. 9, pp. 148698–148709, DOI: 10.1109/ACCESS.2021.3123180.
14. Zhao Q., Wang Z., Wang Y. Fault Location Method for an Active Distribution Network Based on a Hierarchical Optimization Model and Fault Confidence Factors. – Electronics, 2023, 12(6), DOI: 10.3390/electronics12061314.
15. Huang X., Xie Z., Huang X. Fault Location of Distribution Network Base on Improved Cuckoo Search Algorithm. – IEEE Access, 2020, vol. 8, pp. 2272–2283, DOI: 10.1109/ACCESS.2019.2962276.
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18. Bhuiyan E.A. et al. A Deep Learning through DBN Enabled Transmission Line Fault Transient Classification Framework for Multimachine Microgrid Systems. – International Transactions on Electrical Energy Systems, 2022, DOI: 10.1155/2022/6820319.
19. Luo G. et al. Stacked Auto-Encoder-Based Fault Location in Distribution Network. – IEEE Access, 2020, vol. 8, pp. 28043–28053, DOI: 10.1109/ACCESS.2020.2971582.
20. Fan M. et al. Fault Location Method of Distribution Network Based on VGAE-GraphSAGE. – Processes, 2024, vol. 12, DOI: 10.3390/pr12102179.
21. Biswal M., Brahma S.M., Cao H. Supervisory Protection and Automated Event Diagnosis Using PMU Data. – IEEE Transactions on Power Delivery, 2016, 31(4), pp. 1855–1863, DOI:10.1109/TPWRD.2016.2520958.
22. Liu G. et al. Low-Complexity Nonlinear Analysis of Synchrophasor Measurements for Events Detection and Localization. – IEEE Access, 2018, vol. 6, pp. 4982–4993, DOI: 10.1109/ACCESS.2017.2772287.
23. Shu H., Gong Z., Tian X. Fault-Section Identification for Hybrid Distribution Lines Based on Principal Component Analysis. – CSEE Journal of Power and Energy Systems, 2021, vol. 7, No. 3, pp. 591–603, DOI: 10.17775/CSEEJPES.2018.00850
24. Numair M. et al. Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin. – Energies, 2023, vol. 16, DOI: 10.3390/en16237850.
25. Jimenez-Aparicio M., Wilches-Bernal F., Reno M.J. Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data. – IEEE Access, 2023, vol. 11, pp. 74201–74215, DOI: 10.1109/ACCESS.2023.3296737.
26. Yin Z. et al. High-Impedance Fault Section Location for Distribution Networks Based on T-Distributed Stochastic Neighbor Embedding and Variable Mode Decomposition. – Journal of Modern Power Systems and Clean Energy, 2024, vol. 12, No. 5, pp. 1495–1505, DOI: 10.35833/MPCE.2023.000225.
27. Jamali S., Bahmanyar A., Ranjbar S. Hybrid Classifier for Fault Location in Active Distribution Networks. – Protection and Control of Modern Power Systems, 2020, 5(2), DOI: 10.1186/s41601-020-00162-y.
28. Yu Y. et al. Fault Location in Distribution System Using Convolutional Neural Network Based on Domain Transformation. – CSEE Journal of Power and Energy Systems, 2021, vol. 7, No. 3, pp. 472–484, DOI: 10.17775/CSEEJPES.2020.01620.
29. Hu J. et al. Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods. – Journal of Modern Power Systems and Clean Energy, 2023, vol. 11, No. 1, pp. 35–51, DOI: 10.35833/MPCE.2022.000204.
30. Jamei M. et al. Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization. – IEEE Journal on Selected Areas in Communications, 2020, vol. 38, No. 1, pp. 180–192, DOI: 10.1109/JSAC.2019.2951971.
31. Pereira R.A.F. et al. Improved Fault Location on Distribution Feeders Based on Matching During-Fault Voltage Sags. – IEEE Transactions on Power Delivery, 2009, vol. 24, No. 2, pp. 852–862, DOI: 10.1109/TPWRD.2009.2014480.
32. Yuvaraju V., Thangavel S., Golla M. Applications of Artificial Intelligence and PMU Data: A Robust Framework for Precision Fault Location in Transmission Lines. – IEEE Access, 2024, vol. 12, pp. 136565–136587, DOI: 10.1109/ACCESS.2024.3464088.
33. Pandey S., Srivastava A.K., Amidan B.G. A Real Time Event Detection, Classification and Localization Using Synchrophasor Data. – IEEE Transactions on Power Systems, 2020, vol. 35, No. 6, pp. 4421–4431, DOI: 10.1109/TPWRS.2020.2986019.
34. Yoon D.-H., Yoon J. Deep Learning-Based Method for the Robust and Efficient Fault Diagnosis in the Electric Power System. – IEEE Access, 2022, vol. 10, pp. 44660–44668, DOI: 10.1109/ACCESS.2022.3170685.
35. Li M. et al. Fault Identification in Power Network Based on Deep Reinforcement Learning. – CSEE Journal of Power and Energy Systems, 2022, vol. 8, No. 3, pp. 721–731, DOI: 10.17775/CSEEJPES.2020.04520.
36. De Oliveira Neto J.A., Sartori C.A.F., Manassero Junior G. Fault Location in Overhead Transmission Lines Based on Magnetic Signatures and on the Extended Kalman Filter. – IEEE Access, 2021 vol. 9, pp. 15259–15270, DOI: 10.1109/ACCESS.2021.3050211.
37. Majd A.A., Samet H., Ghanbari T. k-NN Based Fault Detection and Classification Methods for Power Transmission Systems. – Protection and Control of Modern Power Systems, 2017, 2(4), DOI: 10.1186/s41601-017-0063-z.
38. Zhang F. et al. Novel Fault Location Method for Power Systems Based on Attention Mechanism and Double Structure GRU Neural Network. – IEEE Access, 2020, vol. 8, pp. 75237–75248, DOI: 10.1109/ACCESS.2020.2988909.
39. Nawaz R. et al. Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning. – IEEE Access, 2024, vol. 12, pp. 93426–93449, DOI: 10.1109/ACCESS.2024.3423828.
40. Mohammadi R., Ghotbi-Maleki M., Ghaffarzadeh A. Wide-Area Fault Location in Transmission Power System Considering Measurement Uncertainty. – IEEE Access, 2024, vol. 12, pp. 42535–42543, DOI: 10.1109/ACCESS.2024.3370921.
41. Senyuk M.D., Klassen V.V., Popovtsev V.V. Adaptive Methodology for Determining Frequency Control Law in Isolated Power Systems Based on Machine Learning Algorithms. – IEEE International Multi-Conference on Engineering, Computer and Information Sciences, 2024, pp. 494–499, DOI: 10.1109/SIBIRCON63777.2024.10758496.
42. Мокеев А.В., Пискунов С.А. Дистанционная защита на основе одностороннего и двухстороннего измерения синхровекторов. – Релейная защита и автоматизация, 2024, № 1 (54), с. 8–15.
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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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7. Fishov A.G. et al. Elektrichestvo – in Russ. (Electricity), 2022, No. 10, pp. 14–24.
8. Von Meier A. et al. Precision Micro-Synchrophasors for Distribution Systems: A Summary of Applications. – IEEE Transactions on Smart Grid, 2017, vol. 8, No. 6, pp. 2926–2936, DOI: 10.1109/TSG.2017.2720543.
9. Korovkin N.V., Bodrenkov E.A. Elektrichestvo – in Russ. (Electricity), 2021, No. 9, pp. 31–37.
10. Gholami M. et al. Detecting the Location of Short-Circuit Faults in Active Distribution Network Using PMU-Based State Estimation. – IEEE Transactions on Smart Grid, 2020, vol. 11, No. 2, pp. 1396–1406, DOI: 10.1109/TSG.2019.293794.
11. Usman M.U., Faruque M.O. Validation of a PMU-Based Fault Location Identification Method for Smart Distribution Network with Photovoltaics Using Real-Time Data. – IET Generation Transmission Distribution, 2018, vol. 12, pp. 5824–5833, DOI: 10.1049/iet-gtd.2018.6245.
12. Chen Z. et al. Time Domain Distribution System Fault Location based on Dynamic State Estimation. – IEEE Power & Energy Society General Meeting (PESGM), 2024, DOI: 10.1109/PESGM51994.2024.10688815.
13. Pang Q. et al. Multi-Timescale-Based Fault Section Location in Distribution Networks. – IEEE Access, 2021, vol. 9, pp. 148698–148709, DOI: 10.1109/ACCESS.2021.3123180.
14. Zhao Q., Wang Z., Wang Y. Fault Location Method for an Active Distribution Network Based on a Hierarchical Optimization Model and Fault Confidence Factors. – Electronics, 2023, 12(6), DOI: 10.3390/electronics12061314.
15. Huang X., Xie Z., Huang X. Fault Location of Distribution Network Base on Improved Cuckoo Search Algorithm. – IEEE Access, 2020, vol. 8, pp. 2272–2283, DOI: 10.1109/ACCESS.2019.2962276.
16. Xie Y. et al. Fault Location in Active Distribution Network Under Hybrid Classical Quantum Computing Architecture. – IEEE Access, 2024, vol. 12, pp. 163924–163937, DOI: 10.1109/ACCESS. 2024.3482308.
17. Li W. et al. A Fully Decentralized Multi-Agent Fault Location and Isolation for Distribution Networks with DGs. – IEEE Access, 2021, vol. 9, pp. 27748–27757, DOI: 10.1109/ACCESS.2021.3058308.
18. Bhuiyan E.A. et al. A Deep Learning through DBN Enabled Transmission Line Fault Transient Classification Framework for Multimachine Microgrid Systems. – International Transactions on Electrical Energy Systems, 2022, DOI: 10.1155/2022/6820319.
19. Luo G. et al. Stacked Auto-Encoder-Based Fault Location in Distribution Network. – IEEE Access, 2020, vol. 8, pp. 28043–28053, DOI: 10.1109/ACCESS.2020.2971582.
20. Fan M. et al. Fault Location Method of Distribution Network Based on VGAE-GraphSAGE. – Processes, 2024, vol. 12, DOI: 10.3390/pr12102179.
21. Biswal M., Brahma S.M., Cao H. Supervisory Protection and Automated Event Diagnosis Using PMU Data. – IEEE Transactions on Power Delivery, 2016, 31(4), pp. 1855–1863, DOI:10.1109/TPWRD. 2016.2520958.
22. Liu G. et al. Low-Complexity Nonlinear Analysis of Synchrophasor Measurements for Events Detection and Localization. – IEEE Access, 2018, vol. 6, pp. 4982–4993, DOI: 10.1109/ACCESS. 2017.2772287.
23. Shu H., Gong Z., Tian X. Fault-Section Identification for Hybrid Distribution Lines Based on Principal Component Analysis. – CSEE Journal of Power and Energy Systems, 2021, vol. 7, No. 3, pp. 591–603, DOI: 10.17775/CSEEJPES.2018.00850
24. Numair M. et al. Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin. – Energies, 2023, vol. 16, DOI: 10.3390/en16237850.
25. Jimenez-Aparicio M., Wilches-Bernal F., Reno M.J. Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data. – IEEE Access, 2023, vol. 11, pp. 74201–74215, DOI: 10.1109/ACCESS.2023.3296737.
26. Yin Z. et al. High-Impedance Fault Section Location for Distribution Networks Based on T-Distributed Stochastic Neighbor Embedding and Variable Mode Decomposition. – Journal of Modern Power Systems and Clean Energy, 2024, vol. 12, No. 5, pp. 1495–1505, DOI: 10.35833/MPCE.2023.000225.
27. Jamali S., Bahmanyar A., Ranjbar S. Hybrid Classifier for Fault Location in Active Distribution Networks. – Protection and Control of Modern Power Systems, 2020, 5(2), DOI: 10.1186/s41601-020-00162-y.
28. Yu Y. et al. Fault Location in Distribution System Using Convolutional Neural Network Based on Domain Transformation. – CSEE Journal of Power and Energy Systems, 2021, vol. 7, No. 3, pp. 472–484, DOI: 10.17775/CSEEJPES.2020.01620.
29. Hu J. et al. Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods. – Journal of Modern Power Systems and Clean Energy, 2023, vol. 11, No. 1, pp. 35–51, DOI: 10.35833/MPCE.2022.000204.
30. Jamei M. et al. Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization. – IEEE Journal on Selected Areas in Communications, 2020, vol. 38, No. 1, pp. 180–192, DOI: 10.1109/JSAC.2019.2951971.
31. Pereira R.A.F. et al. Improved Fault Location on Distribution Feeders Based on Matching During-Fault Voltage Sags. – IEEE Transactions on Power Delivery, 2009, vol. 24, No. 2, pp. 852–862, DOI: 10.1109/TPWRD.2009.2014480.
32. Yuvaraju V., Thangavel S., Golla M. Applications of Artificial Intelligence and PMU Data: A Robust Framework for Precision Fault Location in Transmission Lines. – IEEE Access, 2024, vol. 12, pp. 136565–136587, DOI: 10.1109/ACCESS.2024.3464088.
33. Pandey S., Srivastava A.K., Amidan B.G. A Real Time Event Detection, Classification and Localization Using Synchrophasor Data. – IEEE Transactions on Power Systems, 2020, vol. 35, No. 6, pp. 4421–4431, DOI: 10.1109/TPWRS.2020.2986019.
34. Yoon D.-H., Yoon J. Deep Learning-Based Method for the Robust and Efficient Fault Diagnosis in the Electric Power System. – IEEE Access, 2022, vol. 10, pp. 44660–44668, DOI: 10.1109/ACCESS.2022.3170685.
35. Li M. et al. Fault Identification in Power Network Based on Deep Reinforcement Learning. – CSEE Journal of Power and Energy Systems, 2022, vol. 8, No. 3, pp. 721–731, DOI: 10.17775/CSEEJPES.2020.04520.
36. De Oliveira Neto J.A., Sartori C.A.F., Manassero Junior G. Fault Location in Overhead Transmission Lines Based on Magnetic Signatures and on the Extended Kalman Filter. – IEEE Access, 2021 vol. 9, pp. 15259–15270, DOI: 10.1109/ACCESS.2021.3050211.
37. Majd A.A., Samet H., Ghanbari T. k-NN Based Fault Detection and Classification Methods for Power Transmission Systems. – Protection and Control of Modern Power Systems, 2017, 2(4), DOI: 10.1186/s41601-017-0063-z.
38. Zhang F. et al. Novel Fault Location Method for Power Systems Based on Attention Mechanism and Double Structure GRU Neural Network. – IEEE Access, 2020, vol. 8, pp. 75237–75248, DOI: 10.1109/ACCESS.2020.2988909.
39. Nawaz R. et al. Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning. – IEEE Access, 2024, vol. 12, pp. 93426–93449, DOI: 10.1109/ACCESS.2024.3423828.
40. Mohammadi R., Ghotbi-Maleki M., Ghaffarzadeh A. Wide-Area Fault Location in Transmission Power System Considering Measurement Uncertainty. – IEEE Access, 2024, vol. 12, pp. 42535–42543, DOI: 10.1109/ACCESS.2024.3370921.
41. Senyuk M.D., Klassen V.V., Popovtsev V.V. Adaptive Methodology for Determining Frequency Control Law in Isolated Power Systems Based on Machine Learning Algorithms. – IEEE International Multi-Conference on Engineering, Computer and Information Sciences, 2024, pp. 494–499, DOI: 10.1109/SIBIRCON63777.2024.10758496.
42. Mokeev A.V., Piskunov S.A. Releynaya zashchita i avtomatizatsiya – in Russ. (Relay Protection and Automation), 2024, No. 1 (54), pp. 8–15
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The research was financially supported by the Russian Science Foundation, grant No. 23-79-01024, https://rscf.ru/project/23-79-01024/
2. Аскаров А.Б. и др. Улучшение демпфирующих свойств виртуального синхронного генератора с помощью корректирующего согласно-параллельного регулятора. – Электричество, 2024, № 1, с. 18–36.
3. Суворов А.А. Механизмы возникновения субсинхронных колебаний в энергосистемах с силовыми инверторными преобразователями. Ч. 1. – Электричество, 2025, № 1, с. 17–31.
4. Паздерин А. В. и др. Метод поиска предельного режима в заданном контролируемом сечении с применением потоковой модели. – Электричество, 2024, № 12, с. 49–57.
5. 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 (65), с. 14–24.
6. Senyuk M. et al. Methodology for Forming a Training Sample for Power Systems Emergency Control Algorithm Based on Machine Learning. – Belarusian-Ural-Siberian Smart Energy Conference, 2023, pp. 54–59, DOI: 10.1109/BUSSEC59406.2023.10296257.
7. Фишов А.Г. и др. Активные распределительные электрические сети с децентрализованным мультиагентным управлением режимом. Ч. 1. – Электричество, 2022, № 10, c. 14–24.
8. Von Meier A. et al. Precision Micro-Synchrophasors for Distribution Systems: A Summary of Applications. – IEEE Transactions on Smart Grid, 2017, vol. 8, No. 6, pp. 2926–2936, DOI: 10.1109/TSG.2017.2720543.
9. Коровкин Н.В., Бодренков Е.А. Аналитический расчет узловых нагрузок распределительной электрической сети. – Электричество, 2021, № 9, c. 31–37.
10. Gholami M. et al. Detecting the Location of Short-Circuit Faults in Active Distribution Network Using PMU-Based State Estimation. – IEEE Transactions on Smart Grid, 2020, vol. 11, No. 2, pp. 1396–1406, DOI: 10.1109/TSG.2019.293794.
11. Usman M.U., Faruque M.O. Validation of a PMU-Based Fault Location Identification Method for Smart Distribution Network with Photovoltaics Using Real-Time Data. – IET Generation Transmission Distribution, 2018, vol. 12, pp. 5824–5833, DOI: 10.1049/ietgtd.2018.6245.
12. Chen Z. et al. Time Domain Distribution System Fault Location based on Dynamic State Estimation. – IEEE Power & Energy Society General Meeting (PESGM), 2024, DOI: 10.1109/PESGM51994.2024.10688815.
13. Pang Q. et al. Multi-Timescale-Based Fault Section Location in Distribution Networks. – IEEE Access, 2021, vol. 9, pp. 148698–148709, DOI: 10.1109/ACCESS.2021.3123180.
14. Zhao Q., Wang Z., Wang Y. Fault Location Method for an Active Distribution Network Based on a Hierarchical Optimization Model and Fault Confidence Factors. – Electronics, 2023, 12(6), DOI: 10.3390/electronics12061314.
15. Huang X., Xie Z., Huang X. Fault Location of Distribution Network Base on Improved Cuckoo Search Algorithm. – IEEE Access, 2020, vol. 8, pp. 2272–2283, DOI: 10.1109/ACCESS.2019.2962276.
16. Xie Y. et al. Fault Location in Active Distribution Network Under Hybrid Classical Quantum Computing Architecture. – IEEE Access, 2024, vol. 12, pp. 163924–163937, DOI: 10.1109/ACCESS. 2024.3482308.
17. Li W. et al. A Fully Decentralized Multi-Agent Fault Location and Isolation for Distribution Networks with DGs. – IEEE Access, 2021, vol. 9, pp. 27748–27757, DOI: 10.1109/ACCESS.2021.3058308.
18. Bhuiyan E.A. et al. A Deep Learning through DBN Enabled Transmission Line Fault Transient Classification Framework for Multimachine Microgrid Systems. – International Transactions on Electrical Energy Systems, 2022, DOI: 10.1155/2022/6820319.
19. Luo G. et al. Stacked Auto-Encoder-Based Fault Location in Distribution Network. – IEEE Access, 2020, vol. 8, pp. 28043–28053, DOI: 10.1109/ACCESS.2020.2971582.
20. Fan M. et al. Fault Location Method of Distribution Network Based on VGAE-GraphSAGE. – Processes, 2024, vol. 12, DOI: 10.3390/pr12102179.
21. Biswal M., Brahma S.M., Cao H. Supervisory Protection and Automated Event Diagnosis Using PMU Data. – IEEE Transactions on Power Delivery, 2016, 31(4), pp. 1855–1863, DOI:10.1109/TPWRD.2016.2520958.
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32. Yuvaraju V., Thangavel S., Golla M. Applications of Artificial Intelligence and PMU Data: A Robust Framework for Precision Fault Location in Transmission Lines. – IEEE Access, 2024, vol. 12, pp. 136565–136587, DOI: 10.1109/ACCESS.2024.3464088.
33. Pandey S., Srivastava A.K., Amidan B.G. A Real Time Event Detection, Classification and Localization Using Synchrophasor Data. – IEEE Transactions on Power Systems, 2020, vol. 35, No. 6, pp. 4421–4431, DOI: 10.1109/TPWRS.2020.2986019.
34. Yoon D.-H., Yoon J. Deep Learning-Based Method for the Robust and Efficient Fault Diagnosis in the Electric Power System. – IEEE Access, 2022, vol. 10, pp. 44660–44668, DOI: 10.1109/ACCESS.2022.3170685.
35. Li M. et al. Fault Identification in Power Network Based on Deep Reinforcement Learning. – CSEE Journal of Power and Energy Systems, 2022, vol. 8, No. 3, pp. 721–731, DOI: 10.17775/CSEEJPES.2020.04520.
36. De Oliveira Neto J.A., Sartori C.A.F., Manassero Junior G. Fault Location in Overhead Transmission Lines Based on Magnetic Signatures and on the Extended Kalman Filter. – IEEE Access, 2021 vol. 9, pp. 15259–15270, DOI: 10.1109/ACCESS.2021.3050211.
37. Majd A.A., Samet H., Ghanbari T. k-NN Based Fault Detection and Classification Methods for Power Transmission Systems. – Protection and Control of Modern Power Systems, 2017, 2(4), DOI: 10.1186/s41601-017-0063-z.
38. Zhang F. et al. Novel Fault Location Method for Power Systems Based on Attention Mechanism and Double Structure GRU Neural Network. – IEEE Access, 2020, vol. 8, pp. 75237–75248, DOI: 10.1109/ACCESS.2020.2988909.
39. Nawaz R. et al. Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning. – IEEE Access, 2024, vol. 12, pp. 93426–93449, DOI: 10.1109/ACCESS.2024.3423828.
40. Mohammadi R., Ghotbi-Maleki M., Ghaffarzadeh A. Wide-Area Fault Location in Transmission Power System Considering Measurement Uncertainty. – IEEE Access, 2024, vol. 12, pp. 42535–42543, DOI: 10.1109/ACCESS.2024.3370921.
41. Senyuk M.D., Klassen V.V., Popovtsev V.V. Adaptive Methodology for Determining Frequency Control Law in Isolated Power Systems Based on Machine Learning Algorithms. – IEEE International Multi-Conference on Engineering, Computer and Information Sciences, 2024, pp. 494–499, DOI: 10.1109/SIBIRCON63777.2024.10758496.
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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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19. Luo G. et al. Stacked Auto-Encoder-Based Fault Location in Distribution Network. – IEEE Access, 2020, vol. 8, pp. 28043–28053, DOI: 10.1109/ACCESS.2020.2971582.
20. Fan M. et al. Fault Location Method of Distribution Network Based on VGAE-GraphSAGE. – Processes, 2024, vol. 12, DOI: 10.3390/pr12102179.
21. Biswal M., Brahma S.M., Cao H. Supervisory Protection and Automated Event Diagnosis Using PMU Data. – IEEE Transactions on Power Delivery, 2016, 31(4), pp. 1855–1863, DOI:10.1109/TPWRD. 2016.2520958.
22. Liu G. et al. Low-Complexity Nonlinear Analysis of Synchrophasor Measurements for Events Detection and Localization. – IEEE Access, 2018, vol. 6, pp. 4982–4993, DOI: 10.1109/ACCESS. 2017.2772287.
23. Shu H., Gong Z., Tian X. Fault-Section Identification for Hybrid Distribution Lines Based on Principal Component Analysis. – CSEE Journal of Power and Energy Systems, 2021, vol. 7, No. 3, pp. 591–603, DOI: 10.17775/CSEEJPES.2018.00850
24. Numair M. et al. Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin. – Energies, 2023, vol. 16, DOI: 10.3390/en16237850.
25. Jimenez-Aparicio M., Wilches-Bernal F., Reno M.J. Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data. – IEEE Access, 2023, vol. 11, pp. 74201–74215, DOI: 10.1109/ACCESS.2023.3296737.
26. Yin Z. et al. High-Impedance Fault Section Location for Distribution Networks Based on T-Distributed Stochastic Neighbor Embedding and Variable Mode Decomposition. – Journal of Modern Power Systems and Clean Energy, 2024, vol. 12, No. 5, pp. 1495–1505, DOI: 10.35833/MPCE.2023.000225.
27. Jamali S., Bahmanyar A., Ranjbar S. Hybrid Classifier for Fault Location in Active Distribution Networks. – Protection and Control of Modern Power Systems, 2020, 5(2), DOI: 10.1186/s41601-020-00162-y.
28. Yu Y. et al. Fault Location in Distribution System Using Convolutional Neural Network Based on Domain Transformation. – CSEE Journal of Power and Energy Systems, 2021, vol. 7, No. 3, pp. 472–484, DOI: 10.17775/CSEEJPES.2020.01620.
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30. Jamei M. et al. Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization. – IEEE Journal on Selected Areas in Communications, 2020, vol. 38, No. 1, pp. 180–192, DOI: 10.1109/JSAC.2019.2951971.
31. Pereira R.A.F. et al. Improved Fault Location on Distribution Feeders Based on Matching During-Fault Voltage Sags. – IEEE Transactions on Power Delivery, 2009, vol. 24, No. 2, pp. 852–862, DOI: 10.1109/TPWRD.2009.2014480.
32. Yuvaraju V., Thangavel S., Golla M. Applications of Artificial Intelligence and PMU Data: A Robust Framework for Precision Fault Location in Transmission Lines. – IEEE Access, 2024, vol. 12, pp. 136565–136587, DOI: 10.1109/ACCESS.2024.3464088.
33. Pandey S., Srivastava A.K., Amidan B.G. A Real Time Event Detection, Classification and Localization Using Synchrophasor Data. – IEEE Transactions on Power Systems, 2020, vol. 35, No. 6, pp. 4421–4431, DOI: 10.1109/TPWRS.2020.2986019.
34. Yoon D.-H., Yoon J. Deep Learning-Based Method for the Robust and Efficient Fault Diagnosis in the Electric Power System. – IEEE Access, 2022, vol. 10, pp. 44660–44668, DOI: 10.1109/ACCESS.2022.3170685.
35. Li M. et al. Fault Identification in Power Network Based on Deep Reinforcement Learning. – CSEE Journal of Power and Energy Systems, 2022, vol. 8, No. 3, pp. 721–731, DOI: 10.17775/CSEEJPES.2020.04520.
36. De Oliveira Neto J.A., Sartori C.A.F., Manassero Junior G. Fault Location in Overhead Transmission Lines Based on Magnetic Signatures and on the Extended Kalman Filter. – IEEE Access, 2021 vol. 9, pp. 15259–15270, DOI: 10.1109/ACCESS.2021.3050211.
37. Majd A.A., Samet H., Ghanbari T. k-NN Based Fault Detection and Classification Methods for Power Transmission Systems. – Protection and Control of Modern Power Systems, 2017, 2(4), DOI: 10.1186/s41601-017-0063-z.
38. Zhang F. et al. Novel Fault Location Method for Power Systems Based on Attention Mechanism and Double Structure GRU Neural Network. – IEEE Access, 2020, vol. 8, pp. 75237–75248, DOI: 10.1109/ACCESS.2020.2988909.
39. Nawaz R. et al. Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning. – IEEE Access, 2024, vol. 12, pp. 93426–93449, DOI: 10.1109/ACCESS.2024.3423828.
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41. Senyuk M.D., Klassen V.V., Popovtsev V.V. Adaptive Methodology for Determining Frequency Control Law in Isolated Power Systems Based on Machine Learning Algorithms. – IEEE International Multi-Conference on Engineering, Computer and Information Sciences, 2024, pp. 494–499, DOI: 10.1109/SIBIRCON63777.2024.10758496.
42. Mokeev A.V., Piskunov S.A. Releynaya zashchita i avtomatizatsiya – in Russ. (Relay Protection and Automation), 2024, No. 1 (54), pp. 8–15
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The research was financially supported by the Russian Science Foundation, grant No. 23-79-01024, https://rscf.ru/project/23-79-01024/
