Формирование группового алгоритма дистанционного определения места повреждения воздушной ЛЭП с использованием методов машинного обучения
Ключевые слова:
определение места повреждения, машинное обучение, многофакторный анализ, воздушные линии электропередачи
Аннотация
Статья посвящена анализу возможности повышения точности дистанционного определения места повреждения (ДОМП) воздушных линий электропередачи с использованием методов машинного обучения. Предложены два подхода: адаптивный выбор оптимального метода для конкретных условий аварийного режима и взвешенное усреднение результатов с вероятностными весами. Проанализированы погрешности 11 односторонних и 19 двухсторонних методов ДОМП, выявлены их слабые стороны в зависимости от различных влияющих факторов. Сформирована обучающая выборка на основе имитационного моделирования линии 500 кВ. Для обучения выбраны модели Time Series Forest (TSF) и Hydra Classifier, из которых TSF показала более высокую эффективность. Модель корректно определяет наилучший метод ДОМП в 57 % случаев для односторонних и 71 % – для двухсторонних методов. Взвешенное усреднение снизило приведенную погрешность ДОМП: для односторонних методов в 99 % случаев она не превысила 2,5 %, для двухсторонних – 95 % случаев с погрешностью до 1 %. Сравнение с традиционным усреднением и отдельными методами ДОМП подтвердило преимущество машинного обучения в адаптации к изменяющимся условиям аварийного режима. Все численные результаты получены с применением синхронизированных векторных измерений в качестве аварийных значений тока и напряжения. Применение представленных подходов наиболее актуально для централизованных систем ДОМП, поскольку они позволяют реализовывать множество методов ДОМП.
Литература
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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.
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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.
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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
