Power Transmission Line Fault Detection and Classification

Yusuf Sevim

doi:10.31466/kfbd.1604790

EN TR

Power Transmission Line Fault Detection and Classification

Abstract

It is very important to find short circuit faults of power transmission lines (PTL) quickly and efficiently. Most methods in the literature use classification algorithms for fault detection, but their use in real-time applications increases fault detection time. The reason for this that while the fault detection process is performed with the classification algorithm, the features of incoming data must be extracted continuously by using a window function. In this study, principal component analysis (PCA) or independent component analysis (ICA) algorithms that are suitable for real-time fault detection are proposed to decrease the fault detection time. Besides, time-domain statistical properties of the PTL signals computed over a period of time are proposed to increase classification speed and accuracy. The results show that PCA and ICA algorithms can detect all faults in real-time data streams, and the classification results are 100% for 10 faults with the proposed features.

Keywords

Fault detection, Feature Extraction, Independent Component Analysis, Principal Component Analysis, S Transform.

Elektrik İletim Sistemlerinde Hata Tespiti ve Sınıflandırmösı

Abstract

Elektrik iletim hatlarının (EİH) kısa devre arızalarını hızlı ve etkili bir şekilde tespit etmek çok önemlidir. Literatürdeki çoğu yöntem, arıza tespiti için sınıflandırma algoritmalarını kullanmaktadır, ancak bu yöntemlerin gerçek zamanlı uygulamalarda kullanımı, arıza tespit süresini artırmaktadır. Bunun nedeni, arıza tespit süreci sınıflandırma algoritması ile gerçekleştirilirken, gelen verilerin özelliklerinin sürekli olarak bir pencere fonksiyonu kullanılarak çıkarılması gerektiğidir. Bu çalışmada, arıza tespit süresini azaltmak için gerçek zamanlı arıza tespiti için uygun olan temel bileşen analizi (TBA) veya bağımsız bileşen analizi (BBA) algoritmaları önerilmektedir. Ayrıca, sınıflandırma hızını ve doğruluğunu artırmak için belirli bir zaman aralığında hesaplanan EİH sinyallerinin zaman alanı istatistiksel özellikleri önerilmektedir. Sonuçlar, TBA ve BBA algoritmalarının gerçek zamanlı veri akışlarında tüm arızaları tespit edebildiğini ve önerilen özellikler ile 10 arıza için sınıflandırma sonuçlarının %100 olduğunu göstermektedir.

Keywords

Hata bulma, Özellik Çıkarımı, Bağımsız Bileşen Analizi, Temel Bileşen Analizi, S dönüşümü

References

Abd Allah, R. (2014). Busbar protection scheme based on alienation coefficients for current signals. IJEAT, 3(3), 103–115.
Adhikari, S., Sinha, N., & Dorendrajit, T. (2016). Fuzzy logic based on-line fault detection and classification in transmission line. SpringerPlus. https://doi.org/10.1186/s40064-016-2669-4
Asadi Majd, A., Samet, H., & Ghanbari, T. (2017). k-NN based fault detection and classification methods for power transmission systems. Protection and Control of Modern Power Systems. https://doi.org/10.1186/s41601-017-0063-z
Bakdi, A., & Kouadri, A. (2017). A new adaptive PCA based thresholding scheme for fault detection in complex systems. Chemometrics and Intelligent Laboratory Systems, 162, 83–93.
Bhowmik, P. S., Purkait, P., & Bhattacharya, K. (2009). A novel wavelet transform aided neural network based transmission line fault analysis method. International Journal of Electrical Power and Energy Systems. https://doi.org/10.1016/j.ijepes.2009.01.005
Chatfield, C., & Collins, A. J. (2018). Introduction to multivariate analysis. Introduction to Multivariate Analysis. https://doi.org/10.1201/9780203749999
Fernandes, J. F., Costa, F. B., & De Medeiros, R. P. (2016). Power transformer disturbance classification based on the wavelet transform and artificial neural networks. In Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN.2016.7727260
Godse, R., & Bhat, S. (2020). Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2975431
Guillen, D., Idarraga-Ospina, G., Zamora, A., Paternina, M. R. A., & Ramirez, J. M. (2014). Fault detection and classification in transmission line using the Euclidian Norm of the total WSE. In 2014 IEEE PES Transmission and Distribution Conference and Exposition, PES T and D-LA 2014 - Conference Proceedings. https://doi.org/10.1109/TDC-LA.2014.6955188
Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3), 626–634. https://doi.org/10.1109/72.761722

Jamil, M., Sharma, S. K., & Singh, R. (2015). Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus. https://doi.org/10.1186/s40064-015-1080-x
Kasinathan, G., & Kumarappan, N. (2008). Comparative Study of Fault Identification and Classification on EHV Lines Using Discrete Wavelet Transform and Fourier Transform Based ANN. International Journal of Electrical and Computer Engineering.
Kumar, A., Aditya, Raj, S., Swarnkar, A. K., Barnwal, K., & Debnath, S. (2018). A single ended wavelet based fault classification scheme in transmission line. In Proceedings of 2018 IEEE Applied Signal Processing Conference, ASPCON 2018. https://doi.org/10.1109/ASPCON.2018.8748671
Li, G., Hu, A., Zhang, J., Peng, L., Sun, C., & Cao, D. (2018). High-Agreement Uncorrelated Secret Key Generation Based on Principal Component Analysis Preprocessing. IEEE Transactions on Communications, 66(7). https://doi.org/10.1109/TCOMM.2018.2814607
MacGregor, J. F., Kourti, T., & Nomikos, P. (1996). Analysis, Monitoring and Fault Diagnosis of Industrial Processes Using Multivariate Statistical Projection Methods. IFAC Proceedings Volumes. https://doi.org/10.1016/s1474-6670(17)58632-2
Magagula, X. G., Hamam, Y., Jordaan, J. A., & Yusuff, A. A. (2017). Fault detection and classification method using DWT and SVM in a power distribution network. In Proceedings - 2017 IEEE PES-IAS PowerAfrica Conference: Harnessing Energy, Information and Communications Technology (ICT) for Affordable Electrification of Africa, PowerAfrica 2017. https://doi.org/10.1109/PowerAfrica.2017.7991190
Malla, P., Coburn, W., Keegan, K., & Yu, X. H. (2019). Power System Fault Detection and Classification Using Wavelet Transform and Artificial Neural Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-030-22808-8_27
Martin, E. B., & Morris, A. J. (1996). Non-parametric confidence bounds for process performance monitoring charts. Journal of Process Control. https://doi.org/10.1016/0959-1524(96)00010-8
Moloi, K., & Akumu, A. O. (2019). Power distribution fault diagnostic method based on machine learning technique. In IEEE PES/IAS PowerAfrica Conference: Power Economics and Energy Innovation in Africa, PowerAfrica 2019. https://doi.org/10.1109/PowerAfrica.2019.8928633
Pinnegar, C. R., & Mansinha, L. (2003). The S-transform with windows of arbitrary and varying shape. Geophysics. https://doi.org/10.1190/1.1543223
Pyare Lal Tandan, & Abhijit Mandal. (2015). Identification of Faults and its Location in Transmission Line by using Wavelet Transform. International Journal of Engineering Research And. https://doi.org/10.17577/ijertv4is030654
Roy, N., & Bhattacharya, K. (2015). Detection, classification, and estimation of fault location on an overhead transmission line using s-transform and neural network. Electric Power Components and Systems. https://doi.org/10.1080/15325008.2014.986776
Samantaray, S. R. (2013). A systematic fuzzy rule based approach for fault classification in transmission lines. Applied Soft Computing Journal. https://doi.org/10.1016/j.asoc.2012.09.010
Samantaray, S. R., & Dash, P. K. (2008). Transmission line distance relaying using a variable window short-time Fourier transform. Electric Power Systems Research. https://doi.org/10.1016/j.epsr.2007.05.005
Silva, K. M., Souza, B. A., & Brito, N. S. D. (2006). Fault detection and classification in transmission lines based on wavelet transform and ANN. IEEE Transactions on Power Delivery. https://doi.org/10.1109/TPWRD.2006.876659 Silverman, B. (1986). Density estimation for statistics and data analysis. Chapman and Hall, 37(1), 1–22. https://doi.org/10.2307/2347507
Singh, M., Panigrahi, B. K., & Maheshwari, R. P. (2011). Transmission line fault detection and classification. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology, ICETECT 2011. https://doi.org/10.1109/ICETECT.2011.5760084
SW, Afifi, A. A., & Clark, V. (1997). Computer-Aided Multivariate Analysis. Journal of the American Statistical Association. https://doi.org/10.2307/2965745
Thirumala, K., Kanjolia, A., Jain, T., & Umarikar, A. C. (2020). Empirical wavelet transform and dual feed-forward neural network for classification of power quality disturbances. International Journal of Power and Energy Conversion, 11(1), 1–21.
Upendar, J., Gupta, C. P., & Singh, G. K. (2008). Discrete wavelet transform and probabilistic neural network based algorithm for classification of fault on transmission systems. In 2008 Annual IEEE India Conference (Vol. 1, pp. 206–211).
Yumurtaci, M., Gǒkmen, G., Kocaman, Č., Ergin, S., & Kiliç, O. (2016). Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach. Turkish Journal of Electrical Engineering and Computer Sciences. https://doi.org/10.3906/elk-1312-131
Yusuff, A. A., Jimoh, A. A., & Munda, J. L. (2011). A novel fault features extraction scheme for power transmission line fault diagnosis. In IEEE AFRICON Conference. https://doi.org/10.1109/AFRCON.2011.6072028

Details

Primary Language

English

Subjects

Electrical Circuits and Systems, Electrical Energy Transmission, Networks and Systems, Electronic Sensors

Journal Section

Research Article

Authors

Yusuf Sevim ^*
0000-0001-9649-2465
Türkiye

Publication Date

March 15, 2025

Submission Date

December 23, 2024

Acceptance Date

March 1, 2025

Published in Issue

Year 2025 Volume: 15 Number: 1

DOI

https://doi.org/10.31466/kfbd.1604790

IZ

https://izlik.org/JA52SK94YT

APA

Sevim, Y. (2025). Power Transmission Line Fault Detection and Classification. Karadeniz Fen Bilimleri Dergisi, 15(1), 470-487. https://doi.org/10.31466/kfbd.1604790

AMA

1.Sevim Y. Power Transmission Line Fault Detection and Classification. KFBD. 2025;15(1):470-487. doi:10.31466/kfbd.1604790

Chicago

Sevim, Yusuf. 2025. “Power Transmission Line Fault Detection and Classification”. Karadeniz Fen Bilimleri Dergisi 15 (1): 470-87. https://doi.org/10.31466/kfbd.1604790.

EndNote

Sevim Y (March 1, 2025) Power Transmission Line Fault Detection and Classification. Karadeniz Fen Bilimleri Dergisi 15 1 470–487.

IEEE

[1]Y. Sevim, “Power Transmission Line Fault Detection and Classification”, KFBD, vol. 15, no. 1, pp. 470–487, Mar. 2025, doi: 10.31466/kfbd.1604790.

ISNAD

Sevim, Yusuf. “Power Transmission Line Fault Detection and Classification”. Karadeniz Fen Bilimleri Dergisi 15/1 (March 1, 2025): 470-487. https://doi.org/10.31466/kfbd.1604790.

JAMA

1.Sevim Y. Power Transmission Line Fault Detection and Classification. KFBD. 2025;15:470–487.

MLA

Sevim, Yusuf. “Power Transmission Line Fault Detection and Classification”. Karadeniz Fen Bilimleri Dergisi, vol. 15, no. 1, Mar. 2025, pp. 470-87, doi:10.31466/kfbd.1604790.

Vancouver

1.Yusuf Sevim. Power Transmission Line Fault Detection and Classification. KFBD. 2025 Mar. 1;15(1):470-87. doi:10.31466/kfbd.1604790

The published articles in The Black Sea Journal of Sciences are licensed under Creative Commons Attribution-NonCommercial 4.0 International

Power Transmission Line Fault Detection and Classification

Abstract

Keywords

Elektrik İletim Sistemlerinde Hata Tespiti ve Sınıflandırmösı

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite