Формирование группового алгоритма дистанционного определения места повреждения воздушной ЛЭП с использованием методов машинного обучения
Keywords:
fault location, machine learning, multifactorial analysis, overhead power lines
Abstract
The possibility of improving the accuracy of fault location on overhead power lines using machine learning techniques is analyzed. Two approaches are proposed: adaptive selection of the optimal fault location method for specific emergency conditions and weighted averaging of results using probabilistic weights. The errors of 11 one-side and 19 two-side methods were analyzed, and their weaknesses were revealed depending on various influencing factors. A training dataset was generated based on the 500 kV power line simulation results. The Time Series Forest (TSF) and Hydra Classifier models were selected for training, with TSF demonstrating higher efficiency. The model correctly identifies the optimal fault location method in 57 % of cases for one-side and 71 % for two-side methods. The use of weighted averaging made it possible to decrease the reduced fault location error: for one-side methods, the error did not exceed 2.5 % in 99 % of cases, while for two-side methods, 95 % of cases showed errors below 1 %. Comparison with conventional averaging and individual fault location methods has confirmed the advantage of machine learning in adapting to varying emergency mode conditions. All numerical results have been obtained using phasor measurement data as emergency current and voltage values. The proposed approaches are most relevant for centralized fault location systems, as they enable one to implement many fault location methods.
References
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Исследование выполнено за счет гранта Российского научного фонда № 23-79-01217, https://rscf.ru/project/23-79-01217/
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The research was financially supported by the Russian Science Foundation, grant No. 23-79-01217, https://rscf.ru/project/23-79-01217
2. Zhang X. et al. Power Grid Fault Diagnosis Using Polar PMU Data Plots. – International Journal of Electrical Power & Energy Systems, 2022, vol. 141, DOI: 10.1016/j.ijepes.2022.108148.
3. Saha M.M., Izykowski J., Rosolowski E. Fault Location on Power Networks. London, UK: Springer–Verlag, 2010, 428 p., DOI: 10.1007/978-1-84882-886-5.
4. ДИВГ.57201-08 34 01. ПО «FASTVIEW». Руководство оператора [Электрон. ресурс], URL: https://www.mtrele.ru/shop/programmnoe-obespechenie/fastview.html (дата обращения 25.06.2025).
5. НБРС.421453.002 РЭ. Программно-технический комплекс [Электрон. ресурс], URL: https://inbres.ru/equipments/programmnoe-obespechenie/po-inbres-monitoring/ (дата обращения 25.06.2025).
6. Yablokov A.A. et al. Investigation of Fault Location on Extra-High Voltage Lines with Synchrophasors Based on Real Transient Waveforms. – 5th Int. Youth Conf. on Radio Electronics, Electrical and Power Engineering, 2023, vol. 5, DOI: 10.1109/REEPE57272.2023.10086766.
7. СТО 56947007.29.240.55.224-2016. Методические указания по определению мест повреждений ВЛ напряжением 110 кВ и выше. М.: ПАО «ФСК ЕЭС», 2016, 75 с.
8. Aljohani A. et al. Design and Implementation of an Intelligent Single Line to Ground Fault Locator for Distribution Feeders. – Int. Conf. on Control, Automation and Diagnosis, 2019, DOI: 10.1109/ICCAD46983.2019.9037950.
9. Livani H., Evrenosoglu C.Y., Centeno V.A. A Machine Learning-Based Faulty Line Identification for Smart Distribution Network. – North American Power Symposium, 2013, DOI: 10.1109/TSG.2013.2260421.
10. Hasan A.N., Eboule P.S.P., Twala B. The Use of Machine Learning Techniques to Classify Power Transmission Line Fault Types and Locations. – Int. Conf. on Optimization of Electrical and Electronic Equipment & Intl Aegean Conference on Electrical Machines and Power Electronics, 2017, pp. 221–226, DOI: 10.1109/OPTIM.2017.7974974.
11. De La Cruz Saavedra J.A. et al. Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends. – Energies, 2023, vol. 16 (5), DOI: 10.3390/en16052280.
12. Rezapour H., Jamali S., Bahmanyar A. Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks. – Energies, 2023, vol. 16 (12), DOI: 10.3390/en16124636.
13. Raza A. et al. A Review of Fault Diagnosing Methods in Power Transmission Systems. – Applied Sciences, 2020, vol. 10, DOI: 10.3390/app10041312.
14. Куликов А.Л. и др. Идентификация поврежденного участка воздушной линии электропередачи методом расчета расстояний. – Релейная защита и автоматизация, 2024, т. 2, № 55, с. 36–45.
15. Обалин М.Д. Применение имитационного моделирования для адаптации алгоритмов определения места повреждения линий электропередачи по параметрам аварийного режима: дис. … канд. техн. наук. Н. Новгород, 2016, 200 c.
16. Висящев А.Н. и др. Диагностика состояния воздушных линий электропередачи 10–110 кВ в нормальных и аварийных режимах. Иркутск: Изд-во ИрГТУ, 2012, 270 с.
17. Wilson A.G. Deep Learning is Not So Mysterious or Different. – arXiv, 2025, DOI: 10.48550/arXiv.2503.02113.
18. IEC/IEEE 60255-118-1:2018. Measuring Relays and Protection Equipment. Part 118-1: Synchrophasor for Power Systems, 2018, DOI: 10.1109/IEEESTD.2018.8577045.
19. Das S. et al. Impedance-based Fault Location in Transmission Networks: Theory and Application. – IEEE Access, 2014, vol. 2, pp. 537–557, DOI: 10.1109/ACCESS.2014.2323353.
20. Takagi T. et al. Development of a New Type Fault Locator Using the One-Terminal Voltage and Current Data. – IEEE Transactions on Power Apparatus and Systems, 1982, vol. PAS-101 (8), pp. 2892–2898, DOI: 10.1109/TPAS.1982.317615.
21. Ankamma R.J., Hagos G. Comparison of Accuracy of various Impedance Based Fault Location Algorithms on Power Transmission Line. – International Journal of Science and Research, 2015, vol. 4 (3), pp. 1869–1873, DOI: 10.21275/SUB152473.
22. Avendano O. et al. Tutorial on Fault Locating Embedded in Line Current Differential Relays – Methods, Implementation, and Application Considerations. – 17th Annual Georgia Tech Fault and Disturbance Analysis Conference, 2014, DOI: 20140312/TP6654-01.
23. Устинов А.А. Разработка и совершенствование методов определения места повреждения на трехфазных и четырехфазных воздушных линиях электропередачи высокого напряжения: дис. … канд. тех. наук. Иркутск, 2015, 204 с.
24. Terzija V., Radojevic Z.M., Preston G. Flexible Synchronized Measurement Technology-Based Fault Locator. – IEEE Transactions on Smart Grid, 2015, vol. 6 (2), pp. 866–873, DOI: 10.1109/TSG.2014.2367820.
25. Voloh I., Zhang Z. Fault Locator Based on Line Current Differential Relays Synchronized Measurements. – Texas A&M 63rd Annual Conference for Protective Relay Engineers, 2010, DOI: 10.1109/CPRE.2010.5469521.
26. Zimmerman K., Costello D. Impedance-based Fault Location Experience. – IEEE Rural Electric Power Conference, 2006, DOI: 10.1109/REPCON.2006.1649060.
27. Лямец Ю.Я., Ильин В.А., Подшивалин Н.В. Программный комплекс анализа аварийных процессов и определения места повреждения линии электропередачи. – Электричество, 1996, № 12, с. 2–7.
28. Лямец Ю.Я., Нудельман Г.С., Павлов А.О. Эволюция дистанционной защиты. – Электричество, 1999, № 3, с. 8–15.
29. Шарыгин Д.С. и др. Многофакторное автоматизированное исследование методов определения места повреждения на модели воздушной линии электропередачи 500 кВ. – Вестник ИГЭУ, 2023, № 4, с. 5–17.
30. Sharygin D., Filatova G., Yablokov A. Implementation of Justified Inspection Zone for Fault Location on Overhead Transmission Lines of 110 kV and above. – 6th International Youth Conference on Radio Electronics, Electrical and Power Engineering, 2024, DOI: 10.1109/REEPE60449.2024.10479762.
31. Mukhopadhyay P. et al. Case Study on Fault Analysis Using PMU. – 18th National Power Systems Conference, 2014, DOI: 10.1109/NPSC.2014.7103778.
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Исследование выполнено за счет гранта Российского научного фонда № 23-79-01217, https://rscf.ru/project/23-79-01217/
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The research was financially supported by the Russian Science Foundation, grant No. 23-79-01217, https://rscf.ru/project/23-79-01217
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2025-05-29
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