Araştırma Makalesi
BibTex RIS Kaynak Göster

Determination of Fault Location in Transmission Lines with İmage Processing and Artificial Neural Networks

Yıl 2020, Cilt: 8 Sayı: 3, 678 - 692, 03.09.2020
https://doi.org/10.36306/konjes.678712

Öz

In order to transmit electrical energy in a continuous and quality manner, it is necessary to
control it from the point of production to the point of consumption. Therefore, protection of transmission
and distribution lines is essential at every stage from production to consumption. The main function of
the protection relays in electrical installations should be deactivated as soon as possible in the event of
short circuits in the system. The most important part of the system is energy transmission lines and
distance protection relays that protect these lines. An accurate error location technique is required to
make fast and efficient work. Transformer neutral point grounding in transmission lines affects the
operation of the zero component current during the single phase to ground short circuit failure of a
power system. Considering the relationship between the grounding system and protection systems, an
appropriate grounding choice should be made. Artificial neural network (ANN) has been used in order
to accurately locate short circuit faults in different grounding systems in transmission lines. Compared with support vector machines (SVM) for testing inside ANN The transmission line model is made in the PSCAD ™ / EMTDC ™ simulation program. Data sets were created by recording the image of the impedance change of the R-X impedance diagram of the distance protection relay in short circuit faults created in different grounding systems. The related focal points in the images are given as an introduction to different ANN models using feature extraction and image processing techniques and the ANN model with the highest fault location estimation accuracy was chosen.

Kaynakça

  • Chawla, G., Sachdev, M. S., Ramakrishna, G. (2006). Design, implementation and testing of an artificial neural network based admittance relay. IFAC Proceedings Volumes, 39(7), 125-130.
  • Dos Santos, R. C., Senger, E. C. (2011). Transmission lines distance protection using artificial neural networks. International Journal of Electrical Power & Energy Systems, 33(3), 721-730.
  • Glover, J. D., Sarma, M. S., Overbye, T. J. (2012). Power System Analysis and Design, Stamford: Cengage Learning.
  • Grainger, J. J., Stevenson, W. D., Stevenson, W. D. (2003). Power system analysis.
  • Guangfu, X., Jinxue, G., Chunhe, Z., Qunbing, Y. (2010). The influence of low resistance grounding system in delta side of transformer on differential protection and its solutions. Paper presented at the CICED 2010 Proceedings, China, 1-6, 13-16 September 2010.
  • Jihong, H., Jiali, H., Yaming, S., Li, K. (1993). Accurate fault location method for extra high voltage transmission lines. Paper presented at the 1993 2nd International Conference on Advances in Power System Control, Operation and Management, APSCOM-93, Hong Kong, 189-193, 7-10 Dec. 1993.
  • Jung, H., Park, Y., Han, M., Lee, C., Park, H., Shin, M. (2007). Novel technique for fault location estimation on parallel transmission lines using wavelet. International Journal of Electrical Power & Energy Systems, 29(1), 76-82.
  • Karasu, S., Altan, A., Saraç, Z., Hacıoğlu, R. (2018). Prediction of Bitcoin Prices with Machine Learning Methods using Time Series Data. Paper presented at the Signal Processing and Communications Applications (SIU), IEEE, İzmir, 1-4, 2-5 May 2018.
  • Kırbaş, İ. (2018). İstatistiksel metotlar ve yapay sinir ağları kullanarak kısa dönem çok adımlı rüzgâr hızı tahmini. Sakarya University Journal of Science, 22(1), 24-38.
  • Liang, F., Jeyasurya, B. (2004). Transmission line distance protection using wavelet transform algorithm. IEEE Transactions on Power Delivery, 19(2), 545-553.
  • Liao, Y., Elangovan, S. (1998). Improved symmetrical component-based fault distance estimation for digital distance protection. IEE Proceedings-Generation, Transmission and Distribution, 145(6), 739-746.
  • Lin, X., Ke, S., Gao, Y., Wang, B., Liu, P. (2011). A selective single-phase-to-ground fault protection for neutral un-effectively grounded systems. International Journal of Electrical Power & Energy Systems, 33(4), 1012-1017.
  • Maheshwari, A., Agarwal, V., Sharma, S. K. (2019). Comparative Analysis of ANN-Based FL and Travelling Wave-Based FL for Location of Fault on Transmission Lines. Journal of The Institution of Engineers (India): Series B, 1-10.
  • MathWorks, Train Regression Models in Regression Learner App, https://www.mathworks.com/help/stats/train-regression-models-in-regression-learnerapp. html: ziyaret tarihi: 04 Nisan 2020.
  • Meddeb, A., Amor, N. ve Chebbi, S., 2019, Impact of System Grounding on Distance Relay Operating, 2019 International Conference on Advanced Systems and Emergent Technologies (IC_ASET), 1-5.
  • Osman, A., Malik, O. (2004). Protection of parallel transmission lines using wavelet transform. IEEE Transactions on Power Delivery, 19(1), 49-55.
  • Ram, K., Nirmala, S., Ramesh, K., Vishwakarma, D. (2013). An overview-Protection of Transmission line Using Artificial Intelligence Techniques. International Journal of Engineering Research & Technology (IJERT), 2(1), 1-9.
  • Ray, P. ve Mishra, D. P., 2016, Support vector machine based fault classification and location of a long transmission line, Engineering science and technology, an international journal, 19 (3), 1368-1380.
  • Swetapadma, A. ve Yadav, A., 2018, A novel single-ended fault location scheme for parallel transmission lines using k-nearest neighbor algorithm, Computers & Electrical Engineering, 69, 41-53.
  • Şalvarcı, Ü. B., 2017, Yapay Sinir Ağları Kullanılarak Görüntü İşlemeye Dayalı Ağırlık Tahmini, Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, 83.
  • Yağan, Y. E. (2015). Havai Dağıtım Hatlarında Yapay Sinir Ağları Kullanarak Arıza Analizi. (YÜKSEK LİSANS). Dumlupınar Üniversitesi, Fen Bilimleri Enstitüsü, Kütahya.
  • Yavuz, S., Deveci, M. (2012). İstatiksel Normalizasyon Tekniklerinin Yapay Sinir Ağin Performansina Etkisi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi(40), 167-187.
  • Ye, P., Li, K., Chen, D., David, A. (1998). A novel algorithm for high-resistance earth-fault distance protection. Paper presented at the Proceedings of 1996 Transmission and Distribution Conference and Exposition, California, 475-480, 15-20 September 1996.
  • Zhong, Y., Kang, X., Jiao, Z., Wang, Z., Suonan, J. (2013). A novel distance protection algorithm for the phase-ground fault. IEEE Transactions on Power Delivery, 29(4), 1718-1725.
  • Ziegler, G. (2011). Numerical distance protection: principles and applications: John Wiley & Sons.
  • Zubić, S., Balcerek, P., Zeljković, Č. (2017). Speed and security improvements of distance protection based on Discrete Wavelet and Hilbert transform. Electric Power Systems Research, 148, 27-34.

GÖRÜNTÜ İŞLEME VE YAPAY SİNİR AĞLARI İLE İLETİM HATLARINDA ARIZA YERİ BELİRLEME

Yıl 2020, Cilt: 8 Sayı: 3, 678 - 692, 03.09.2020
https://doi.org/10.36306/konjes.678712

Öz

Elektrik enerjisinin kesintisiz ve kaliteli bir şekilde iletilmesi için, üretim yapıldığı noktadan tüketim
olan noktaya kadar kontrol edilmesi gerekmektedir. Dolayısıyla üretimden tüketime kadar her aşamada
iletim ve dağıtım hatlarında koruma yapılması şarttır. Elektrik tesislerinde koruma rölelerinin temel
görevi, sistemde meydana gelen kısa devrelerde arızalı olan bölgenin mümkün olan en kısa sürede devre
dışı etmektir. Sistemin en önemli parçası olan enerji iletim hatları ve bu hatları koruyan mesafe koruma
rölelerine bu konuda çok önemli görevler düşmektedir. Hızlı ve verimli çalışmalar yapmak için doğru
bir hata yeri tespit tekniği gereklidir. İletim hatlarında transformatör nötr nokta topraklaması bir güç
sisteminin tek faz – toprak kısa devre arızası sırasında oluşan sıfır bileşen akımı mesafe koruma rölesinin
çalışmasını etkilemektedir. Topraklama sistemi ve koruma sistemleri arasındaki ilişki göz önüne
alındığında, uygun bir topraklama seçimi yapılmalıdır. İletim hatlarında farklı topraklama sistemlerinde
kısa devre arızalarının yerinin doğru bir şekilde belirlenebilmesi için yapay sinir ağı (YSA) kullanılmıştır.
YSA’nın performansını test etmek için destek vektör makineleri (DVM) ile karşılaştırılmıştır. İletim hattı
modeli PSCAD ™ / EMTDC ™ benzetim programında oluşturulup YSA için gerekli veriler elde
edilmiştir. Farklı topraklama sistemlerinde oluşturulan kısa devre arızalarındaki mesafe koruma
rölesinin R-X empedans diyagramının empedans değişiminin görüntüsü kayıt altına alınarak veri setleri
oluşturulmuştur. Görüntülerde ilgili odak noktaları özellik çıkarım ve görüntü işleme teknikleri
kullanılarak farklı YSA modellerine giriş olarak verilmiş ve en iyi arıza yeri tahmini veren YSA modeli
seçilmiştir.

Kaynakça

  • Chawla, G., Sachdev, M. S., Ramakrishna, G. (2006). Design, implementation and testing of an artificial neural network based admittance relay. IFAC Proceedings Volumes, 39(7), 125-130.
  • Dos Santos, R. C., Senger, E. C. (2011). Transmission lines distance protection using artificial neural networks. International Journal of Electrical Power & Energy Systems, 33(3), 721-730.
  • Glover, J. D., Sarma, M. S., Overbye, T. J. (2012). Power System Analysis and Design, Stamford: Cengage Learning.
  • Grainger, J. J., Stevenson, W. D., Stevenson, W. D. (2003). Power system analysis.
  • Guangfu, X., Jinxue, G., Chunhe, Z., Qunbing, Y. (2010). The influence of low resistance grounding system in delta side of transformer on differential protection and its solutions. Paper presented at the CICED 2010 Proceedings, China, 1-6, 13-16 September 2010.
  • Jihong, H., Jiali, H., Yaming, S., Li, K. (1993). Accurate fault location method for extra high voltage transmission lines. Paper presented at the 1993 2nd International Conference on Advances in Power System Control, Operation and Management, APSCOM-93, Hong Kong, 189-193, 7-10 Dec. 1993.
  • Jung, H., Park, Y., Han, M., Lee, C., Park, H., Shin, M. (2007). Novel technique for fault location estimation on parallel transmission lines using wavelet. International Journal of Electrical Power & Energy Systems, 29(1), 76-82.
  • Karasu, S., Altan, A., Saraç, Z., Hacıoğlu, R. (2018). Prediction of Bitcoin Prices with Machine Learning Methods using Time Series Data. Paper presented at the Signal Processing and Communications Applications (SIU), IEEE, İzmir, 1-4, 2-5 May 2018.
  • Kırbaş, İ. (2018). İstatistiksel metotlar ve yapay sinir ağları kullanarak kısa dönem çok adımlı rüzgâr hızı tahmini. Sakarya University Journal of Science, 22(1), 24-38.
  • Liang, F., Jeyasurya, B. (2004). Transmission line distance protection using wavelet transform algorithm. IEEE Transactions on Power Delivery, 19(2), 545-553.
  • Liao, Y., Elangovan, S. (1998). Improved symmetrical component-based fault distance estimation for digital distance protection. IEE Proceedings-Generation, Transmission and Distribution, 145(6), 739-746.
  • Lin, X., Ke, S., Gao, Y., Wang, B., Liu, P. (2011). A selective single-phase-to-ground fault protection for neutral un-effectively grounded systems. International Journal of Electrical Power & Energy Systems, 33(4), 1012-1017.
  • Maheshwari, A., Agarwal, V., Sharma, S. K. (2019). Comparative Analysis of ANN-Based FL and Travelling Wave-Based FL for Location of Fault on Transmission Lines. Journal of The Institution of Engineers (India): Series B, 1-10.
  • MathWorks, Train Regression Models in Regression Learner App, https://www.mathworks.com/help/stats/train-regression-models-in-regression-learnerapp. html: ziyaret tarihi: 04 Nisan 2020.
  • Meddeb, A., Amor, N. ve Chebbi, S., 2019, Impact of System Grounding on Distance Relay Operating, 2019 International Conference on Advanced Systems and Emergent Technologies (IC_ASET), 1-5.
  • Osman, A., Malik, O. (2004). Protection of parallel transmission lines using wavelet transform. IEEE Transactions on Power Delivery, 19(1), 49-55.
  • Ram, K., Nirmala, S., Ramesh, K., Vishwakarma, D. (2013). An overview-Protection of Transmission line Using Artificial Intelligence Techniques. International Journal of Engineering Research & Technology (IJERT), 2(1), 1-9.
  • Ray, P. ve Mishra, D. P., 2016, Support vector machine based fault classification and location of a long transmission line, Engineering science and technology, an international journal, 19 (3), 1368-1380.
  • Swetapadma, A. ve Yadav, A., 2018, A novel single-ended fault location scheme for parallel transmission lines using k-nearest neighbor algorithm, Computers & Electrical Engineering, 69, 41-53.
  • Şalvarcı, Ü. B., 2017, Yapay Sinir Ağları Kullanılarak Görüntü İşlemeye Dayalı Ağırlık Tahmini, Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, 83.
  • Yağan, Y. E. (2015). Havai Dağıtım Hatlarında Yapay Sinir Ağları Kullanarak Arıza Analizi. (YÜKSEK LİSANS). Dumlupınar Üniversitesi, Fen Bilimleri Enstitüsü, Kütahya.
  • Yavuz, S., Deveci, M. (2012). İstatiksel Normalizasyon Tekniklerinin Yapay Sinir Ağin Performansina Etkisi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi(40), 167-187.
  • Ye, P., Li, K., Chen, D., David, A. (1998). A novel algorithm for high-resistance earth-fault distance protection. Paper presented at the Proceedings of 1996 Transmission and Distribution Conference and Exposition, California, 475-480, 15-20 September 1996.
  • Zhong, Y., Kang, X., Jiao, Z., Wang, Z., Suonan, J. (2013). A novel distance protection algorithm for the phase-ground fault. IEEE Transactions on Power Delivery, 29(4), 1718-1725.
  • Ziegler, G. (2011). Numerical distance protection: principles and applications: John Wiley & Sons.
  • Zubić, S., Balcerek, P., Zeljković, Č. (2017). Speed and security improvements of distance protection based on Discrete Wavelet and Hilbert transform. Electric Power Systems Research, 148, 27-34.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Serkan Budak

Bahadir Akbal 0000-0002-7319-1966

Yayımlanma Tarihi 3 Eylül 2020
Gönderilme Tarihi 22 Ocak 2020
Kabul Tarihi 5 Mayıs 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 3

Kaynak Göster

IEEE S. Budak ve B. Akbal, “GÖRÜNTÜ İŞLEME VE YAPAY SİNİR AĞLARI İLE İLETİM HATLARINDA ARIZA YERİ BELİRLEME”, KONJES, c. 8, sy. 3, ss. 678–692, 2020, doi: 10.36306/konjes.678712.