Using Machine Learning and Artificial Neural Networks to Recognize Inter-Turn Faults in Power Transformers
Keywords:
inter-turn faults in power transformers, artificial neural network (ANN), sensitivity, speed of response, multiparametric relay protection
Abstract
The power transformer relay protection is among those featuring the poorest selectivity. It is only in 70–80 % of fault cases that the relay protection responds in a correct and timely manner, preventing further development of the emergency. One of the main problems is the recognition of internal faults (inter-turn faults in the transformer winding). Neural network algorithms are suggested used to be used to recognize inter-turn faults in transformers. Simulation-type Matlab models have been developed to study the operation of a transformer as part of an electrical network in normal mode, as well as in case of inter-turn faults. Based on the simulation results, a large amount of statistical data has been obtained that serves to train an artificial neural network (ANN). Two options of transformer protection aimed at recognizing inter-turn faults are proposed: (i) the use of ANN directly for decision making and (ii) the use of ANN to calculate the coefficients of multiparametric relay protection. The proposed protection options were tested using neural network algorithms. It has been determined that the second protection option is more preferable for implementation, because the ANN is used only for calculating the weighting factors. In comparison with the classical differential protection, the proposed ANN-based protection is distinguished by better recognition of inter-turn winding faults with a small number of short-circuited turns at the early fault development stages.
References
1. Perez L.G., et al. Training an Artificial Neural Network to Discriminate Between Magnetizing Inrushand Internal Faults. – IEEE Transactions on Power Delivery, 1994, vol. 9, No. 1, pp. 434–441, DOI: 10.1109/61.277715.
2. Куликов А.Л. и др. Применение метода наложения для решения задачи определения места повреждения в сетях среднего напряжения. – Электричество, 2021. № 9. с. 38–44.
3. Balaga H., Vishwakarma D.N, Sinha A. Application of ANN Based Pattern Recognition Technique for the Protection of 3-Phase Power Transformer. – SEMCCO, 2011, pp. 358–365, DOI:10.1007/978-3-642-27172-4_44.
4. Лоскутов А.А., Пелевин П.С., Митрович М. Разработка логической части интеллектуальной многопараметрической релейной защиты. – Электричество, 2020, № 5, с. 12–18.
5. Souza J.R.M.S., et al. Designing and Training an Artificial Neural Network Fed by Harmonic Content of Differential Currents for Inrush Detection. – International Conference on Power Systems Transients, 2017.
6. Shah B.S., Parmar S.B. Transformer Protection Using Artificial Neural Network. – International Journal of Novel Research and Development, 2017, vol. 2, iss. 5, pp. 108–111.
7. Tripathy M. Power Transformer Differential Protection Based on Hindawi Neural Network Principal Component Analysis, Harmonic Restraint and Park’s Plots. – Advances in Artificial Intelligence, 2012, DOI:10.1155/2012/930740.
8. Rebizant W., Bejmert D. Transformer Differential Protection with Neural Network Based Inrush Stabilization. – Power Tech, 2007, DOI:10.1109/PCT.2007.4538488.
9. Jamil M., Singh R., Sharma S.K. Fault Detection and Classification in Electrical Power Transmission System Using Artificial Neural Network. – SpringerPlus, 2015, 4(1), 334, DOI:10.1186/s40064-015-1080-x.
10. Лоскутов А.А., Митрович М., Осокин В.Ю. Повышение распознаваемости режимов функционирования системы электроснабжения на основе методов машинного обучения. – Релейная защита и автоматизация, 2020. № 4 (41), с. 26–34.
11. Santos R., Senger E.C. Transmission Lines Distance Protection Using Artificial Neural Networks. – International Journal of Electrical Power & Energy Systems, 2011, 33(3), pp. 721–730, DOI:10.1016/j.ijepes.2010.12.029.
12. Barreira M.L.A. Neural Networks Improving the Performance of the Distance Protection. – Theses, 2013, 77 p.
13. Altaie A.S. Design of a New Digital Relay for Transmission Line Fault Detection, Classification and Localization Based on a New Composite Relay and Artificial Neural Network Approach. – Master’s Thesis., 2015, 652, 113 p., https://scholarworks.wmich.edu/masters_theses/652.
14. Megahed A., Abou-Ghazala A., Hanafy M. A Novel Protective Scheme for Double Circuit Transmission Line Based on Modular Neural Network of Combined Structure. – Przegląd Elektrotechniczny, 2012, pp. 233–237, R. 88 NR 10a/2012.
15. Ayyagari S.B. Artificial Neural Network Based Fault Location for Transmission Lines. – University of Kentucky Master's Theses, 2011, 117 p.
16. Колобанов П.А., Куликов А.Л. Совершенствование аналитических методов исследования внутренних замыканий в двухобмоточных трансформаторах для целей релейной защиты. – Вестник ИГЭУ, 2019, № 1, с. 18–30.
17. Лямец Ю.Я. и др. Эффекты многомерности в релейной защите. – Электричество, 2011, № 9, с. 48–54.
18. Галушкин А.И. Синтез многослойных систем распознавания образов. М.: Энергия, 1974, 366 с.
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Исследование выполнено при финансовой поддержке в рамках программы «Приоритет 2030»
#
1. Perez L.G., et al. Training an Artificial Neural Network to Discriminate Between Magnetizing Inrushand Internal Faults. – IEEE Transactions on Power Delivery, 1994, vol. 9, No. 1, pp. 434–441, DOI: 10.1109/61.277715.
2. Kulikov A.L., et al. Elektrichestvo – in Russ. (Electricity), 2021. No. 9. pp. 38–44.
3. Balaga H., Vishwakarma D.N, Sinha A. Application of ANN Based Pattern Recognition Technique for the Protection of 3-Phase Power Transformer. – SEMCCO, 2011, pp. 358–365, DOI:10.1007/978-3-642-27172-4_44.
4. Loskutov A.A., Pelevin P.S., Mitrovich M. Elektrichestvo – in Russ. (Electricity), 2020, No. 5. pp. 12–18.
5. Souza J.R.M.S., et al. Designing and Training an Artificial Neural Network Fed by Harmonic Content of Differential Currents for Inrush Detection. – International Conference on Power Systems Transients, 2017.
6. Shah B.S., Parmar S.B. Transformer Protection Using Artificial Neural Network. – International Journal of Novel Research and Development, 2017, vol. 2, iss. 5, pp. 108–111.
7. Tripathy M. Power Transformer Differential Protection Based on Hindawi Neural Network Principal Component Analysis, Harmonic Restraint and Park’s Plots. – Advances in Artificial Intelligence, 2012, DOI:10.1155/2012/930740.
8. Rebizant W., Bejmert D. Transformer Differential Protection with Neural Network Based Inrush Stabilization. – Power Tech, 2007, DOI:10.1109/PCT.2007.4538488.
9. Jamil M., Singh R., Sharma S.K. Fault Detection and Classifica-tion in Electrical Power Transmission System Using Artificial Neural Network. – SpringerPlus, 2015, 4(1), 334, DOI:10.1186/s40064-015-1080-x.
10. Loskutov A.A., Mitrovich M., Osokin V.Yu. Releynaya zashchita i avtomatizatsiya – in Russ. (Relay Protection and Automation), 2020, No. 4 (41). pp. 26–34.
11. Santos R., Senger E.C. Transmission Lines Distance Protection Using Artificial Neural Networks. – International Journal of Electrical Power & Energy Systems, 2011, 33(3), pp. 721–730, DOI:10.1016/j.ijepes.2010.12.029.
12. Barreira M.L.A. Neural Networks Improving the Performance of the Distance Protection. – Theses, 2013, 77 p.
13. Altaie A.S. Design of a New Digital Relay for Transmission Line Fault Detection, Classification and Localization Based on a New Composite Relay and Artificial Neural Network Approach. – Master’s Thesis., 2015, 652, 113 p., https://scholarworks.wmich.edu/masters_theses/652.
14. Megahed A., Abou-Ghazala A., Hanafy M. A Novel Protective Scheme for Double Circuit Transmission Line Based on Modular Neural Network of Combined Structure. – Przegląd Elektrotechniczny, 2012, pp. 233–237, R. 88 NR 10a/2012.
15. Ayyagari S.B. Artificial Neural Network Based Fault Location for Transmission Lines. – University of Kentucky Master's Theses, 2011, 117 p.
16. Kolobanov P.A., Kulikov A.L. Vestnik IGEU – in Russ. (Vestnik of ISPEU), 2019, No. 1, pp. 18–30.
17. Lyamets Yu.Ya., et al. Elektrichestvo – in Russ. (Electricity), 2011, No. 9, pp. 48–54.
18. Galushkin А.I. Sintez mnogosloynyh sistem raspoznavaniya obrazov (Synthesis of Multilayer Pattern Recognition Systems). М.: Energiya, 1974, 366 p
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The study was carried out with financial support under the "Priority 2030" program
2. Куликов А.Л. и др. Применение метода наложения для решения задачи определения места повреждения в сетях среднего напряжения. – Электричество, 2021. № 9. с. 38–44.
3. Balaga H., Vishwakarma D.N, Sinha A. Application of ANN Based Pattern Recognition Technique for the Protection of 3-Phase Power Transformer. – SEMCCO, 2011, pp. 358–365, DOI:10.1007/978-3-642-27172-4_44.
4. Лоскутов А.А., Пелевин П.С., Митрович М. Разработка логической части интеллектуальной многопараметрической релейной защиты. – Электричество, 2020, № 5, с. 12–18.
5. Souza J.R.M.S., et al. Designing and Training an Artificial Neural Network Fed by Harmonic Content of Differential Currents for Inrush Detection. – International Conference on Power Systems Transients, 2017.
6. Shah B.S., Parmar S.B. Transformer Protection Using Artificial Neural Network. – International Journal of Novel Research and Development, 2017, vol. 2, iss. 5, pp. 108–111.
7. Tripathy M. Power Transformer Differential Protection Based on Hindawi Neural Network Principal Component Analysis, Harmonic Restraint and Park’s Plots. – Advances in Artificial Intelligence, 2012, DOI:10.1155/2012/930740.
8. Rebizant W., Bejmert D. Transformer Differential Protection with Neural Network Based Inrush Stabilization. – Power Tech, 2007, DOI:10.1109/PCT.2007.4538488.
9. Jamil M., Singh R., Sharma S.K. Fault Detection and Classification in Electrical Power Transmission System Using Artificial Neural Network. – SpringerPlus, 2015, 4(1), 334, DOI:10.1186/s40064-015-1080-x.
10. Лоскутов А.А., Митрович М., Осокин В.Ю. Повышение распознаваемости режимов функционирования системы электроснабжения на основе методов машинного обучения. – Релейная защита и автоматизация, 2020. № 4 (41), с. 26–34.
11. Santos R., Senger E.C. Transmission Lines Distance Protection Using Artificial Neural Networks. – International Journal of Electrical Power & Energy Systems, 2011, 33(3), pp. 721–730, DOI:10.1016/j.ijepes.2010.12.029.
12. Barreira M.L.A. Neural Networks Improving the Performance of the Distance Protection. – Theses, 2013, 77 p.
13. Altaie A.S. Design of a New Digital Relay for Transmission Line Fault Detection, Classification and Localization Based on a New Composite Relay and Artificial Neural Network Approach. – Master’s Thesis., 2015, 652, 113 p., https://scholarworks.wmich.edu/masters_theses/652.
14. Megahed A., Abou-Ghazala A., Hanafy M. A Novel Protective Scheme for Double Circuit Transmission Line Based on Modular Neural Network of Combined Structure. – Przegląd Elektrotechniczny, 2012, pp. 233–237, R. 88 NR 10a/2012.
15. Ayyagari S.B. Artificial Neural Network Based Fault Location for Transmission Lines. – University of Kentucky Master's Theses, 2011, 117 p.
16. Колобанов П.А., Куликов А.Л. Совершенствование аналитических методов исследования внутренних замыканий в двухобмоточных трансформаторах для целей релейной защиты. – Вестник ИГЭУ, 2019, № 1, с. 18–30.
17. Лямец Ю.Я. и др. Эффекты многомерности в релейной защите. – Электричество, 2011, № 9, с. 48–54.
18. Галушкин А.И. Синтез многослойных систем распознавания образов. М.: Энергия, 1974, 366 с.
---
Исследование выполнено при финансовой поддержке в рамках программы «Приоритет 2030»
#
1. Perez L.G., et al. Training an Artificial Neural Network to Discriminate Between Magnetizing Inrushand Internal Faults. – IEEE Transactions on Power Delivery, 1994, vol. 9, No. 1, pp. 434–441, DOI: 10.1109/61.277715.
2. Kulikov A.L., et al. Elektrichestvo – in Russ. (Electricity), 2021. No. 9. pp. 38–44.
3. Balaga H., Vishwakarma D.N, Sinha A. Application of ANN Based Pattern Recognition Technique for the Protection of 3-Phase Power Transformer. – SEMCCO, 2011, pp. 358–365, DOI:10.1007/978-3-642-27172-4_44.
4. Loskutov A.A., Pelevin P.S., Mitrovich M. Elektrichestvo – in Russ. (Electricity), 2020, No. 5. pp. 12–18.
5. Souza J.R.M.S., et al. Designing and Training an Artificial Neural Network Fed by Harmonic Content of Differential Currents for Inrush Detection. – International Conference on Power Systems Transients, 2017.
6. Shah B.S., Parmar S.B. Transformer Protection Using Artificial Neural Network. – International Journal of Novel Research and Development, 2017, vol. 2, iss. 5, pp. 108–111.
7. Tripathy M. Power Transformer Differential Protection Based on Hindawi Neural Network Principal Component Analysis, Harmonic Restraint and Park’s Plots. – Advances in Artificial Intelligence, 2012, DOI:10.1155/2012/930740.
8. Rebizant W., Bejmert D. Transformer Differential Protection with Neural Network Based Inrush Stabilization. – Power Tech, 2007, DOI:10.1109/PCT.2007.4538488.
9. Jamil M., Singh R., Sharma S.K. Fault Detection and Classifica-tion in Electrical Power Transmission System Using Artificial Neural Network. – SpringerPlus, 2015, 4(1), 334, DOI:10.1186/s40064-015-1080-x.
10. Loskutov A.A., Mitrovich M., Osokin V.Yu. Releynaya zashchita i avtomatizatsiya – in Russ. (Relay Protection and Automation), 2020, No. 4 (41). pp. 26–34.
11. Santos R., Senger E.C. Transmission Lines Distance Protection Using Artificial Neural Networks. – International Journal of Electrical Power & Energy Systems, 2011, 33(3), pp. 721–730, DOI:10.1016/j.ijepes.2010.12.029.
12. Barreira M.L.A. Neural Networks Improving the Performance of the Distance Protection. – Theses, 2013, 77 p.
13. Altaie A.S. Design of a New Digital Relay for Transmission Line Fault Detection, Classification and Localization Based on a New Composite Relay and Artificial Neural Network Approach. – Master’s Thesis., 2015, 652, 113 p., https://scholarworks.wmich.edu/masters_theses/652.
14. Megahed A., Abou-Ghazala A., Hanafy M. A Novel Protective Scheme for Double Circuit Transmission Line Based on Modular Neural Network of Combined Structure. – Przegląd Elektrotechniczny, 2012, pp. 233–237, R. 88 NR 10a/2012.
15. Ayyagari S.B. Artificial Neural Network Based Fault Location for Transmission Lines. – University of Kentucky Master's Theses, 2011, 117 p.
16. Kolobanov P.A., Kulikov A.L. Vestnik IGEU – in Russ. (Vestnik of ISPEU), 2019, No. 1, pp. 18–30.
17. Lyamets Yu.Ya., et al. Elektrichestvo – in Russ. (Electricity), 2011, No. 9, pp. 48–54.
18. Galushkin А.I. Sintez mnogosloynyh sistem raspoznavaniya obrazov (Synthesis of Multilayer Pattern Recognition Systems). М.: Energiya, 1974, 366 p
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The study was carried out with financial support under the "Priority 2030" program
Published
2022-04-11
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