Development of a Device for Diagnosing Unbalanced Short Circuits in 10 kV Overhead Power Lines
Keywords:
electrical networks, power supply reliability, short circuit, unbalanced mode, Arduino, artificial neural network, classification, protection, real time
Abstract
The article addresses the development, based on the Arduino platform, of an intelligent microprocessor device intended to determine the type and place of an unbalanced short circuit fault in 10 kV overhead power lines. The device analyzes phase currents and voltages, as well as their zero and negative sequence components. An algorithm based on a pre-trained neural network of the multilayer perceptron type (MLP) has been implemented, which is capable of classifying, in real time, 12 different types of short circuit faults, including single-phase, two-phase and three-phase short circuits, as well as ground faults. The neural network was trained, based on the data obtained, in the MATLAB/Simulink environment, followed by integrating the algorithm into the Arduino environment. Laboratory tests of the mock-up with simulation of emergency modes have confirmed high accuracy (more than 95%), high response speed (the response time is less than 80 ms), and stability of the device performance. The hardware solution features compactness, low power consumption, simple configuration, and the ability to visualize the result. The proposed device can be effectively integrated into local relay protection and monitoring systems. Its application is especially relevant in 6-10 kV distribution networks under the conditions of limited financing, remoteness of facilities and the need to improve the power supply reliability.
References
1. Аллаев К.Р., Холиддинов И.Х., Холиддинова М.М. Методология оценки эффективности электрических сетей с использованием нечеткой логики. – Электричество, 2025, № 2, с. 4–14.
2. Кудрин Б.И. Об энергетической стратегии и энергетической безопасности России. – Промышленная энергетика, 2008, № 12, с. 2–7.
3. Aslan Y., Yağan Y.E. Artificial Neural-Network-Based Fault Location for Power Distribution Lines Using the Frequency Spectra of Fault Data. – Electrical Engineering, 2017, vol. 99, pp. 301–311, DOI: 10.1007/s00202-016-0428-8.
4. Santos A.S. et al. Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation. – Sensors, 2022, vol. 22, DOI: 10.3390/s22239418.
5. Su C. et al. A Two-Terminal Fault Location Fusion Model of Transmission Line Based on CNN-Multi-Head-LSTM with an Attention Module. – Energies, 2023, vol. 16, No. 4, DOI: 10.3390/en16041827.
6. Javadian S.A.M., Haghifam M.-R., Rezaei N. A Fault Location and Protection Scheme for Distribution Systems in Presence of DG Using MLP Neural Networks. – IEEE Power & Energy Society General Meeting, 2009, DOI: 10.1109/PES.2009.5275863.
7. Ning K. et al. A Dual-Path Neural Network for High-Impedance Fault Detection. – Mathematics, 2025, vol. 13(2), DOI: 10.3390/math13020225.
8. Budak S., Akbal B. Estimation of High Impedance Fault Location in Electrical Transmission Lines Using Artificial Neural Networks and R-X Impedance Graph. 2020, DOI: 10.48550/arXiv.2011.03227.
9. Li W. et al. Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks. – IEEE Transactions on Power Systems, 2018, vol. 34, No. 6, pp. 4640–4651, DOI: 10.1109/TPWRS.2019.2917794.
10. Koley E., Verma K., Ghosh S. An Improved Fault Detection Classification and Location Scheme Based on Wavelet Transform and Modular ANN for Six Phase Lines. – SpringerPlus, 2015, vol. 4 (1), DOI: 10.1186/s40064-015-1342-7.
11. Sarvi M., Torabi S.M. Determination of Fault Location and Type in Distribution Systems using Clark Transformation and Neural Network. – International Journal of Applied Power Engineering, 2012, vol. 1, No. 2, pp. 75–86, DOI: 10.11591/ijape.v1i2.1678.
12. Pat. US 6385561 B1. Automatic Fault Location in Cabling Systems / J.J. Soraghan, W.H. Siew, 2002.
13. А.с. SU 185405 A1. Способ определения расстояния до места повреждения линии электропередачи и связи / Г.М. Шалыт, 1966.
14. Пат. RU 2269789 C1. Способ определения места повреждения линий электропередачи и связи и устройство для его осуществления / А.Л. Куликов, Д.А. Куликов, 2006.
15. Пат. RU 2319972 C1. Способ определения наличия дефектов проводов и кабелей в сегментах сетей с разветвленной топологией / А.В. Карпов, А.Н. Закиров, 2008.
16. Пат. UZ FAP № 01166. Устройство для регистрации дополнительных потерь электроэнергии при несимметрии нагрузок в низковольтных электрических сетях / К.Р. Аллаев, С.Э. Шаисматов, И.Х. Ходиддинов, 2016.
17. Сидоренко О.А. О методах определения симметричных составляющих напряжений и токов по результатам измерений. – Сборник трудов магистрантов Донецкого национального технического университета, 2005, вып. 4.
18. Жежеленко И.В. Показатели качества электроэнергии и их контроль на промышленных предприятиях. М.: Энергоатомиздат, 1986, 168 с.
19. Kholiddinov I., Hоliddinоvа M. Modeling a Block to Determine the Type of Damage to a Power Line Using MATLAB. – Scientific Research, 2025, vol. 5, No. 2, pp. 157–164, DOI: 10.36719/ 2789-6919/42/157-164.
#
1. Allaev K.R., Holiddinov I.H., Holiddinova M.M. Elektrichestvo – in Russ. (Electricity), 2025, No. 2, pp. 4–14.
2. Kudrin B.I. Promyshlennaya energetika – in Russ. (Industrial Power Engineering), 2008, No. 12, pp. 2–7.
3. Aslan Y., Yağan Y.E. Artificial Neural-Network-Based Fault Location for Power Distribution Lines Using the Frequency Spectra of Fault Data. – Electrical Engineering, 2017, vol. 99, pp. 301–311, DOI: 10.1007/s00202-016-0428-8.
4. Santos A.S. et al. Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation. – Sensors, 2022, vol. 22, DOI: 10.3390/s22239418.
5. Su C. et al. A Two-Terminal Fault Location Fusion Model of Transmission Line Based on CNN-Multi-Head-LSTM with an Attention Module. – Energies, 2023, vol. 16, No. 4, DOI: 10.3390/en16041827.
6. Javadian S.A.M., Haghifam M.-R., Rezaei N. A Fault Location and Protection Scheme for Distribution Systems in Presence of DG Using MLP Neural Networks. – IEEE Power & Energy Society General Meeting, 2009, DOI: 10.1109/PES.2009.5275863.
7. Ning K. et al. A Dual-Path Neural Network for High-Impedance Fault Detection. – Mathematics, 2025, vol. 13(2), DOI: 10.3390/math13020225.
8. Budak S., Akbal B. Estimation of High Impedance Fault Location in Electrical Transmission Lines Using Artificial Neural Networks and R-X Impedance Graph. 2020, DOI: 10.48550/arXiv.2011.03227.
9. Li W. et al. Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks. – IEEE Transactions on Power Systems, 2018, vol. 34, No. 6, pp. 4640–4651, DOI: 10.1109/TPWRS.2019.2917794.
10. Koley E., Verma K., Ghosh S. An Improved Fault Detection Classification and Location Scheme Based on Wavelet Transform and Modular ANN for Six Phase Lines. – SpringerPlus, 2015, vol. 4 (1), DOI: 10.1186/s40064-015-1342-7.
11. Sarvi M., Torabi S.M. Determination of Fault Location and Type in Distribution Systems using Clark Transformation and Neural Network. – International Journal of Applied Power Engineering, 2012, vol. 1, No. 2, pp. 75–86, DOI: 10.11591/ijape.v1i2.1678.
12. Pat. US 6385561 B1. Automatic Fault Location in Cabling Systems / J.J. Soraghan, W.H. Siew, 2002.
13. A.s. SU 185405 A1. Sposob opredeleniya rasstoyaniya do mesta povrezhdeniya linii elektroperedachi i svyazi (A Method for Determining the Distance to the Location of Damage to Power Lines and Communications) / G.M. Shalyt, 1966.
14. Pat. RU 2269789 C1. Sposob opredeleniya mesta povrezh-deniya liniy elektroperedachi i svyazi i ustroystvo dlya ego osushchestveleniya (A Method for Determining the Location of Damage to Power Transmission and Communication Lines and a Device for Its Implementation) / A.L. Kulikov, D.A. Kulikov, 2006.
15. Pat. RU 2319972 C1. Sposob opredeleniya nalichiya defektov provodov i kabeley v segmentah setey s razvetvlennoy topologiey (A Method for Determining the Presence of Defects in Wires and Cables in Network Segments with an Extensive Topology) / A.V. Karpov, A.N. Zakirov, 2008.
16. Pat. UZ FAP No. 01166. Ustroystvo dlya registratsii dopolnitel'nyh poter' elektroenergii pri nesimmetrii nagruzok v nizkovol'tnyh elektricheskih setyah (A Device for Recording Additional Power Losses in Case of Load Asymmetry in LV Electrical Networks) / K.R. Allaev, S.E. Shaismatov, I.H. Hodiddinov, 2016.
17. Sidorenko O.A. Sbornik trudov magistrantov Donetskogo natsional’nogo tehnicheskogo universiteta – in Russ. (Proceedings of Master Students of Donetsk National Technical University), 2005, iss. 4.
18. Zhezhelenko I.V. Pokazateli kachestva elektroenergii i ih kontrol’ na promyshlennyh predpriyatiyah (Electricity Quality Indicators and Their Monitoring at Industrial Enterprises). M.: Energoatomizdat, 1986, 168 p.
19. Kholiddinov I., Holiddinova M. Modeling a Block to Determine the Type of Damage to a Power Line Using MATLAB. – Scientific Research, 2025, vol. 5, No. 2, pp. 157–164, DOI: 10.36719/ 2789-6919/42/157-164
2. Кудрин Б.И. Об энергетической стратегии и энергетической безопасности России. – Промышленная энергетика, 2008, № 12, с. 2–7.
3. Aslan Y., Yağan Y.E. Artificial Neural-Network-Based Fault Location for Power Distribution Lines Using the Frequency Spectra of Fault Data. – Electrical Engineering, 2017, vol. 99, pp. 301–311, DOI: 10.1007/s00202-016-0428-8.
4. Santos A.S. et al. Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation. – Sensors, 2022, vol. 22, DOI: 10.3390/s22239418.
5. Su C. et al. A Two-Terminal Fault Location Fusion Model of Transmission Line Based on CNN-Multi-Head-LSTM with an Attention Module. – Energies, 2023, vol. 16, No. 4, DOI: 10.3390/en16041827.
6. Javadian S.A.M., Haghifam M.-R., Rezaei N. A Fault Location and Protection Scheme for Distribution Systems in Presence of DG Using MLP Neural Networks. – IEEE Power & Energy Society General Meeting, 2009, DOI: 10.1109/PES.2009.5275863.
7. Ning K. et al. A Dual-Path Neural Network for High-Impedance Fault Detection. – Mathematics, 2025, vol. 13(2), DOI: 10.3390/math13020225.
8. Budak S., Akbal B. Estimation of High Impedance Fault Location in Electrical Transmission Lines Using Artificial Neural Networks and R-X Impedance Graph. 2020, DOI: 10.48550/arXiv.2011.03227.
9. Li W. et al. Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks. – IEEE Transactions on Power Systems, 2018, vol. 34, No. 6, pp. 4640–4651, DOI: 10.1109/TPWRS.2019.2917794.
10. Koley E., Verma K., Ghosh S. An Improved Fault Detection Classification and Location Scheme Based on Wavelet Transform and Modular ANN for Six Phase Lines. – SpringerPlus, 2015, vol. 4 (1), DOI: 10.1186/s40064-015-1342-7.
11. Sarvi M., Torabi S.M. Determination of Fault Location and Type in Distribution Systems using Clark Transformation and Neural Network. – International Journal of Applied Power Engineering, 2012, vol. 1, No. 2, pp. 75–86, DOI: 10.11591/ijape.v1i2.1678.
12. Pat. US 6385561 B1. Automatic Fault Location in Cabling Systems / J.J. Soraghan, W.H. Siew, 2002.
13. А.с. SU 185405 A1. Способ определения расстояния до места повреждения линии электропередачи и связи / Г.М. Шалыт, 1966.
14. Пат. RU 2269789 C1. Способ определения места повреждения линий электропередачи и связи и устройство для его осуществления / А.Л. Куликов, Д.А. Куликов, 2006.
15. Пат. RU 2319972 C1. Способ определения наличия дефектов проводов и кабелей в сегментах сетей с разветвленной топологией / А.В. Карпов, А.Н. Закиров, 2008.
16. Пат. UZ FAP № 01166. Устройство для регистрации дополнительных потерь электроэнергии при несимметрии нагрузок в низковольтных электрических сетях / К.Р. Аллаев, С.Э. Шаисматов, И.Х. Ходиддинов, 2016.
17. Сидоренко О.А. О методах определения симметричных составляющих напряжений и токов по результатам измерений. – Сборник трудов магистрантов Донецкого национального технического университета, 2005, вып. 4.
18. Жежеленко И.В. Показатели качества электроэнергии и их контроль на промышленных предприятиях. М.: Энергоатомиздат, 1986, 168 с.
19. Kholiddinov I., Hоliddinоvа M. Modeling a Block to Determine the Type of Damage to a Power Line Using MATLAB. – Scientific Research, 2025, vol. 5, No. 2, pp. 157–164, DOI: 10.36719/ 2789-6919/42/157-164.
#
1. Allaev K.R., Holiddinov I.H., Holiddinova M.M. Elektrichestvo – in Russ. (Electricity), 2025, No. 2, pp. 4–14.
2. Kudrin B.I. Promyshlennaya energetika – in Russ. (Industrial Power Engineering), 2008, No. 12, pp. 2–7.
3. Aslan Y., Yağan Y.E. Artificial Neural-Network-Based Fault Location for Power Distribution Lines Using the Frequency Spectra of Fault Data. – Electrical Engineering, 2017, vol. 99, pp. 301–311, DOI: 10.1007/s00202-016-0428-8.
4. Santos A.S. et al. Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation. – Sensors, 2022, vol. 22, DOI: 10.3390/s22239418.
5. Su C. et al. A Two-Terminal Fault Location Fusion Model of Transmission Line Based on CNN-Multi-Head-LSTM with an Attention Module. – Energies, 2023, vol. 16, No. 4, DOI: 10.3390/en16041827.
6. Javadian S.A.M., Haghifam M.-R., Rezaei N. A Fault Location and Protection Scheme for Distribution Systems in Presence of DG Using MLP Neural Networks. – IEEE Power & Energy Society General Meeting, 2009, DOI: 10.1109/PES.2009.5275863.
7. Ning K. et al. A Dual-Path Neural Network for High-Impedance Fault Detection. – Mathematics, 2025, vol. 13(2), DOI: 10.3390/math13020225.
8. Budak S., Akbal B. Estimation of High Impedance Fault Location in Electrical Transmission Lines Using Artificial Neural Networks and R-X Impedance Graph. 2020, DOI: 10.48550/arXiv.2011.03227.
9. Li W. et al. Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks. – IEEE Transactions on Power Systems, 2018, vol. 34, No. 6, pp. 4640–4651, DOI: 10.1109/TPWRS.2019.2917794.
10. Koley E., Verma K., Ghosh S. An Improved Fault Detection Classification and Location Scheme Based on Wavelet Transform and Modular ANN for Six Phase Lines. – SpringerPlus, 2015, vol. 4 (1), DOI: 10.1186/s40064-015-1342-7.
11. Sarvi M., Torabi S.M. Determination of Fault Location and Type in Distribution Systems using Clark Transformation and Neural Network. – International Journal of Applied Power Engineering, 2012, vol. 1, No. 2, pp. 75–86, DOI: 10.11591/ijape.v1i2.1678.
12. Pat. US 6385561 B1. Automatic Fault Location in Cabling Systems / J.J. Soraghan, W.H. Siew, 2002.
13. A.s. SU 185405 A1. Sposob opredeleniya rasstoyaniya do mesta povrezhdeniya linii elektroperedachi i svyazi (A Method for Determining the Distance to the Location of Damage to Power Lines and Communications) / G.M. Shalyt, 1966.
14. Pat. RU 2269789 C1. Sposob opredeleniya mesta povrezh-deniya liniy elektroperedachi i svyazi i ustroystvo dlya ego osushchestveleniya (A Method for Determining the Location of Damage to Power Transmission and Communication Lines and a Device for Its Implementation) / A.L. Kulikov, D.A. Kulikov, 2006.
15. Pat. RU 2319972 C1. Sposob opredeleniya nalichiya defektov provodov i kabeley v segmentah setey s razvetvlennoy topologiey (A Method for Determining the Presence of Defects in Wires and Cables in Network Segments with an Extensive Topology) / A.V. Karpov, A.N. Zakirov, 2008.
16. Pat. UZ FAP No. 01166. Ustroystvo dlya registratsii dopolnitel'nyh poter' elektroenergii pri nesimmetrii nagruzok v nizkovol'tnyh elektricheskih setyah (A Device for Recording Additional Power Losses in Case of Load Asymmetry in LV Electrical Networks) / K.R. Allaev, S.E. Shaismatov, I.H. Hodiddinov, 2016.
17. Sidorenko O.A. Sbornik trudov magistrantov Donetskogo natsional’nogo tehnicheskogo universiteta – in Russ. (Proceedings of Master Students of Donetsk National Technical University), 2005, iss. 4.
18. Zhezhelenko I.V. Pokazateli kachestva elektroenergii i ih kontrol’ na promyshlennyh predpriyatiyah (Electricity Quality Indicators and Their Monitoring at Industrial Enterprises). M.: Energoatomizdat, 1986, 168 p.
19. Kholiddinov I., Holiddinova M. Modeling a Block to Determine the Type of Damage to a Power Line Using MATLAB. – Scientific Research, 2025, vol. 5, No. 2, pp. 157–164, DOI: 10.36719/ 2789-6919/42/157-164
Published
2025-07-21
Issue
Section
Article
