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Karma İletim Hatlarında Görüntü İşleme Kullanılarak Karşılaştırmalı Hata Konumu Tahmini

Year 2020, Volume: 8 , 62 - 75, 31.12.2020
https://doi.org/10.36306/konjes.821726

Abstract

Havai hatlar genellikle elektrik enerjisi iletimi için kullanılır. Ayrıca XLPE yeraltı kablo hatları genellikle şehir merkezinde ve kalabalık alanlarda elektrik güvenliğini sağlamak için kullanılır, bu nedenle iletim hatlarında havai hat ile birlikte yüksek gerilim yeraltı kablo hatları kullanılır ve bu hatlar karma hatları olarak adlandırılır. Mesafe koruma röleleri, iletim hatlarındaki akım ve gerilim büyüklüklerine göre empedans tabanlı ölçüm sonucu arıza yerini belirler. Ancak yüksek gerilim kablo hattının karakteristik empedansı havai hattan önemli ölçüde farklı olduğundan, birim uzunluk başına farklı karakteristik empedans nedeniyle karma iletim hatlarında arıza konumu doğru bir şekilde tespit edilemez. Bu nedenle karma iletim hatlarında mesafe koruma röleleri ile arıza bölümünün ve yerinin tespiti zordur. Bu çalışmada, 154 kV havai iletim hattı ve yer altı kablo hattı, mesafe koruma röleleri için karma iletim hattı olarak incelenmiştir. Karma iletim hattında faz-toprak arızaları oluşturulur ve havai hat bölümü ve yeraltı kablo bölümü PSCAD / EMTDC ™ kullanılarak benzetimi yapılmıştır. Kısa devre arıza görüntüleri, havai iletim hattı ve yer altı kablo iletim hattı arızaları için mesafe koruma rölesinde oluşturulur. Görüntüler, arızanın R-X empedans diyagramını içerir ve R-X empedans diyagramından elde edilen görüntüler görüntü işleme adımları uygulanmıştır. Arıza yeri tahmini için görüntü işleme sonuçlarından çıkarılan özellikler giriş parametresi olarak belirlenmiştir. Yapay sinir ağları (YSA) ve regresyon yöntemleri kullanılarak arıza yeri tahmini yapılmıştır. YSA sonuçları ve regresyon yöntemleri bu çalışmanın sonunda iletim hatlarında arıza yerinin tahmin edilmesi için en uygun yöntemin seçilmesi için karşılaştırılmıştır.

References

  • Aziz, M. M. A., khalil Ibrahim, D., & Gilany, M. (2006). Fault location scheme for combined overhead line with underground power cable. Electric Power Systems Research, 76(11), 928-935.
  • Budak, S. (2020). Karma iletim hatlarında mesafe koruma rölesi çalışmasının incelenmesi ve çalışma başarımlarının yükseltilmesi. (Yüksek Lisans). Konya Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü, Konya.
  • Budak, S., & Akbal, B. Fault Location Estimation by Using Machine Learning Methods in Mixed Transmission Lines. Avrupa Bilim ve Teknoloji Dergisi, 245-250.
  • Ekici, S. (2012). Support Vector Machines for classification and locating faults on transmission lines. Applied soft computing, 12(6), 1650-1658.
  • Fan, R., Yin, T., Huang, R., Lian, J., & Wang, S. (2019). Transmission Line Fault Location Using Deep Learning Techniques. Paper presented at the 2019 North American Power Symposium (NAPS).
  • Glover, J. D., Sarma, M. S., & Overbye, T. J. (2012). Power System. Analysis and Design, Stamford: Cengage Learning.
  • Han, J., & Crossley, P. A. (2013). Fault location on mixed overhead line and cable transmission networks. Paper presented at the 2013 IEEE Grenoble Conference.
  • Han, J., & Crossley, P. A. (2015). Traveling wave fault locator for mixed, overhead, and underground teed transmission feeders. IEEJ Transactions on Electrical and Electronic Engineering, 10(4), 383-389.
  • Karasu, S., Altan, A., Saraç, Z., & Hacioğlu, R. (2018). Prediction of Bitcoin prices with machine learning methods using time series data. Paper presented at the 2018 26th Signal Processing and Communications Applications Conference (SIU).
  • Khorashadi-Zadeh, H. (2004). Artificial neural network approach to fault classification for double circuit transmission lines. Paper presented at the 2004 IEEE/PES Transmision and Distribution Conference and Exposition: Latin America (IEEE Cat. No. 04EX956).
  • Livani, H., & Evrenosoglu, C. Y. (2013). A machine learning and wavelet-based fault location method for hybrid transmission lines. IEEE Transactions on Smart Grid, 5(1), 51-59.
  • MathWorks. Choose Regression Model Options. Retrieved from https://www.mathworks.com/help/stats/choose-regression-model-options.html
  • MathWorks. Train Regression Models in Regression Learner App. Retrieved from https://www.mathworks.com/help/stats/regression-learner-app.html#:~:text=Choose%20among%20various%20algorithms%20to,Models%20in%20Regression%20Learner%20App.
  • Niazy, I., & Sadeh, J. (2013). A new single ended fault location algorithm for combined transmission line considering fault clearing transients without using line parameters. International Journal of Electrical Power & Energy Systems, 44(1), 816-823.
  • Özleyen, Ü. (2019). Hibrit güç sistemlerinde arıza tespiti. (Yüksek Lisans). Fırat Üniversitesi Fen Bilimleri Enstitüsü, Elezığ.
  • Öztemel, E. (2012). Yapay Sinir Ağları, Papatya Yayıncılık Eğitim, 3. Basım, İstanbul.
  • Thukaram, D., Khincha, H., & Vijaynarasimha, H. (2005). Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Transactions on Power Delivery, 20(2), 710-721.
  • Tziouvaras, D. (2006). Protection of high-voltage AC cables. Paper presented at the 59th Annual Conference for Protective Relay Engineers, 2006.
  • Yatendra, K., Tripathi, P., & Singh, R. (2019). Impact of FACTS Device on Zonal Protection Scheme in Modified Dorsey-Chicago Transmission System. Paper presented at the 2019 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE).
  • Yegnanarayana, B. (2009). Artificial neural networks: PHI Learning Pvt. Ltd.

COMPARATIVE FAULT LOCATION ESTIMATION BY USING IMAGE PROCESSING IN MIXED TRANSMISSION LINES

Year 2020, Volume: 8 , 62 - 75, 31.12.2020
https://doi.org/10.36306/konjes.821726

Abstract

Overhead lines are generally used for electrical energy transmission. Also, XLPE underground cable lines are generally used in the city center and the crowded areas to provide electrical safety, so high voltage underground cable lines are used together with overhead line in the transmission lines, and these lines are called as the mixed lines. The distance protection relays are used to determine the impedance based fault location according to the current and voltage magnitudes in the transmission lines. However, the fault location cannot be correctly detected in mixed transmission lines due to different characteristic impedance per unit length because the characteristic impedance of high voltage cable line is significantly different from overhead line. Thus, determinations of the fault section and location with the distance protection relays are difficult in the mixed transmission lines. In this study, 154 kV overhead transmission line and underground cable line are examined as the mixed transmission line for the distance protection relays. Phase to ground faults are created in the mixed transmission line. overhead line section and underground cable section are simulated by using PSCAD/ EMTDC ™. The short circuit fault images are generated in the distance protection relay for the overhead transmission line and underground cable transmission line faults. The images include the R-X impedance diagram of the fault, and the R-X impedance diagram have been detected by applying image processing steps. Artificial neural network (ANN) and the regression methods are used for prediction of the fault location, and the results of image processing are used as the input parameters for the training process of ANN and the regression methods. The results of ANN and regression methods are compared to select the most suitable method at the end of this study for forecasting of the fault location in transmission lines.

References

  • Aziz, M. M. A., khalil Ibrahim, D., & Gilany, M. (2006). Fault location scheme for combined overhead line with underground power cable. Electric Power Systems Research, 76(11), 928-935.
  • Budak, S. (2020). Karma iletim hatlarında mesafe koruma rölesi çalışmasının incelenmesi ve çalışma başarımlarının yükseltilmesi. (Yüksek Lisans). Konya Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü, Konya.
  • Budak, S., & Akbal, B. Fault Location Estimation by Using Machine Learning Methods in Mixed Transmission Lines. Avrupa Bilim ve Teknoloji Dergisi, 245-250.
  • Ekici, S. (2012). Support Vector Machines for classification and locating faults on transmission lines. Applied soft computing, 12(6), 1650-1658.
  • Fan, R., Yin, T., Huang, R., Lian, J., & Wang, S. (2019). Transmission Line Fault Location Using Deep Learning Techniques. Paper presented at the 2019 North American Power Symposium (NAPS).
  • Glover, J. D., Sarma, M. S., & Overbye, T. J. (2012). Power System. Analysis and Design, Stamford: Cengage Learning.
  • Han, J., & Crossley, P. A. (2013). Fault location on mixed overhead line and cable transmission networks. Paper presented at the 2013 IEEE Grenoble Conference.
  • Han, J., & Crossley, P. A. (2015). Traveling wave fault locator for mixed, overhead, and underground teed transmission feeders. IEEJ Transactions on Electrical and Electronic Engineering, 10(4), 383-389.
  • Karasu, S., Altan, A., Saraç, Z., & Hacioğlu, R. (2018). Prediction of Bitcoin prices with machine learning methods using time series data. Paper presented at the 2018 26th Signal Processing and Communications Applications Conference (SIU).
  • Khorashadi-Zadeh, H. (2004). Artificial neural network approach to fault classification for double circuit transmission lines. Paper presented at the 2004 IEEE/PES Transmision and Distribution Conference and Exposition: Latin America (IEEE Cat. No. 04EX956).
  • Livani, H., & Evrenosoglu, C. Y. (2013). A machine learning and wavelet-based fault location method for hybrid transmission lines. IEEE Transactions on Smart Grid, 5(1), 51-59.
  • MathWorks. Choose Regression Model Options. Retrieved from https://www.mathworks.com/help/stats/choose-regression-model-options.html
  • MathWorks. Train Regression Models in Regression Learner App. Retrieved from https://www.mathworks.com/help/stats/regression-learner-app.html#:~:text=Choose%20among%20various%20algorithms%20to,Models%20in%20Regression%20Learner%20App.
  • Niazy, I., & Sadeh, J. (2013). A new single ended fault location algorithm for combined transmission line considering fault clearing transients without using line parameters. International Journal of Electrical Power & Energy Systems, 44(1), 816-823.
  • Özleyen, Ü. (2019). Hibrit güç sistemlerinde arıza tespiti. (Yüksek Lisans). Fırat Üniversitesi Fen Bilimleri Enstitüsü, Elezığ.
  • Öztemel, E. (2012). Yapay Sinir Ağları, Papatya Yayıncılık Eğitim, 3. Basım, İstanbul.
  • Thukaram, D., Khincha, H., & Vijaynarasimha, H. (2005). Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Transactions on Power Delivery, 20(2), 710-721.
  • Tziouvaras, D. (2006). Protection of high-voltage AC cables. Paper presented at the 59th Annual Conference for Protective Relay Engineers, 2006.
  • Yatendra, K., Tripathi, P., & Singh, R. (2019). Impact of FACTS Device on Zonal Protection Scheme in Modified Dorsey-Chicago Transmission System. Paper presented at the 2019 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE).
  • Yegnanarayana, B. (2009). Artificial neural networks: PHI Learning Pvt. Ltd.
There are 20 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Serkan Budak 0000-0002-6125-1634

Bahadir Akbal 0000-0002-7319-1966

Publication Date December 31, 2020
Submission Date November 5, 2020
Acceptance Date December 22, 2020
Published in Issue Year 2020 Volume: 8

Cite

IEEE S. Budak and B. Akbal, “COMPARATIVE FAULT LOCATION ESTIMATION BY USING IMAGE PROCESSING IN MIXED TRANSMISSION LINES”, KONJES, vol. 8, pp. 62–75, 2020, doi: 10.36306/konjes.821726.