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A model based on bag of visual words to predict the category of damage in XLPE insulation under the application of combined AC and repeated lightning impulses of both polarities

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Abstract

Cross-linked polyethylene (XLPE) is employed in high-voltage transmission cables, due to their distinguished insulation performance. Localized degradation due to AC voltages is a major cause of damage in these systems. These damages known as electrical trees vary in their shape and characteristics according to the cause of damage. This paper discusses the effect of lightning overvoltages in trees formed in these insulation systems. Initially experiments have been conducted to generate trees at 10 kV (tree-like tree) and 12 kV (bush branch tree) and the structure of the trees are visualized. This work is extended to study the effect of lightning overvoltages. It is crucial to detect the type of damage as accurately as possible. In this research work, a classification tree model, which is based on the Bag of Visual Words (BoVW), has been developed to predict the category of damage. The accuracy of the proposed model is evaluated using Receiver Operating Characteristics (ROCs) curves and confusion matrix. Finally, the validity of the developed model is verified by comparing it with the in-house collected experimental data and the other State-of-the-Art feature extraction techniques.

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References

  1. Parpal JL, Crine JP, Dang C (1997) Electrical aging of extruded dielectric cables: a physical model. IEEE Trans Dielectr Electr Insul 4:197–209. https://doi.org/10.1109/94.595247

    Article  Google Scholar 

  2. R. Sarathi, K.H. Oza, C.L.G. Pavan Kumar, T. Tanaka, Electrical treeing in XLPE cable insulation under harmonic AC voltages, IEEE Trans. Dielectr. Electr. Insul. 22 (2015) 3177–3185. https://doi.org/10.1109/TDEI.2015.005022.

  3. Du BX, Zhu LW (2015) Electrical tree characteristics of XLPE under repetitive pulse voltage in low temperature. IEEE Trans Dielectr Electr Insul 22:1801–1808. https://doi.org/10.1109/TDEI.2015.005183

    Article  Google Scholar 

  4. Ispirli MM, Ersoy Yilmaz A, Kalenderli Ö (2018) Investigation of tracking phenomenon in cable joints as 3D with finite element method. Electr. Eng. 100:2193–2203. https://doi.org/10.1007/s00202-018-0696-6

    Article  Google Scholar 

  5. Ghoneim SSM, Dessouky SS, Elfaraskoury AA, Sharaf ABA (2018) Prediction of insulating transformer oils breakdown voltage considering barrier effect based on artificial neural networks. Electr Eng 100:2231–2242. https://doi.org/10.1007/s00202-018-0697-5

    Article  Google Scholar 

  6. Chen G, Tham CH (2009) Electrical treeing characteristics in XLPE power cable insulation in frequency range between 20 and 500 Hz. IEEE Trans Dielectr Electr Insul 16:179–188. https://doi.org/10.1109/TDEI.2009.4784566

    Article  Google Scholar 

  7. Liu H, Liu Y, Li Y, Zheng P, Rui H (2017) Growth and partial discharge characteristics of electrical tree in XLPE under AC-DC composite voltage. IEEE Trans Dielectr Electr Insul 24:2282–2290. https://doi.org/10.1109/TDEI.2017.006537

    Article  Google Scholar 

  8. Sarathi R, Sheema IPM, Abirami R (2013) Partial discharge source classification by support vector machine. In: 2013 IEEE 1st int. conf. cond. assess. tech. electr. syst. IEEE CATCON 2013—proc. https://doi.org/10.1109/CATCON.2013.6737508

  9. Yoshimura N, Yanagiwara M, Fan LG (1991) Diagnostics of treeing degradation by image processing. IEEE Trans Electr Insul 26:314–317. https://doi.org/10.1109/14.78334

    Article  Google Scholar 

  10. Balaji SP, Sheema IPM, Krithika G, Usa S (2011) Effect of repeated impulses on transformer insulation. IEEE Trans Dielectr Electr Insul. https://doi.org/10.1109/TDEI.2011.6118645

    Article  Google Scholar 

  11. Ying L, Xiaolong C (2014) A novel method for the insulation thickness design of HV XLPE cable based on electrical treeing tests. IEEE Trans Dielectr Electr Insul 21:1540–1546. https://doi.org/10.1109/TDEI.2014X.004250

    Article  Google Scholar 

  12. Xiao B, Wang K, Bi X, Li W, Han J (2019) 2D-LBP: an enhanced local binary feature for texture image classification. IEEE Trans Circuits Syst Video Technol 29:2796–2808. https://doi.org/10.1109/TCSVT.2018.2869841

    Article  Google Scholar 

  13. El-Zein A, Talaat M, El Bahy M (2009) A numerical model of electrical tree growth in solid insulation. IEEE Trans Dielectr Electr Insul 16:1724–1734. https://doi.org/10.1109/TDEI.2009.5361596

    Article  Google Scholar 

  14. Schurch R et al (2017) Calculating the fractal dimension from 3D images of electrical trees. IEEE Int Symp High Voltage Eng (ISH), pp 4–9

  15. Filliat D (2007) A visual bag of words method for interactive qualitative localization and mapping. In: Proceedings - IEEE International Conference on Robotics and Automation, pp 3921–3926

  16. Raju PM, Mishra D, Gorthi RKSS (2019) Detection based long term tracking in correlation filter trackers. Pattern Recogn Lett 122:79–85. https://doi.org/10.1016/j.patrec.2019.02.028

    Article  Google Scholar 

  17. Pires R, Jelinek HF, Wainer J, Valle E, Rocha A (2014) Advancing bag-of-visual-words representations for lesion classification in retinal images. PLoS ONE 9(6):e96814. https://doi.org/10.1371/journal.pone.0096814

    Article  Google Scholar 

  18. Salahat E, Qasaimeh M (2017) Recent advances in features extraction and description algorithms: a comprehensive survey. In: Proc. IEEE int. conf. ind. technol. pp, 1059–1063. https://doi.org/10.1109/ICIT.2017.7915508

  19. Bay H, Tuytelaars T, Van Gool L (2006) LNCS 3951—SURF: speeded up robust features, comput. vision–ECCV 2006, pp 404–417. https://doi.org/10.1007/11744023_32

  20. Peng X, Wang L, Wang X, Qiao Y (2016) Bag of visual words and fusion methods for action recognition: comprehensive study and good practice. Comput Vis Image Underst 150:109–125. https://doi.org/10.1016/j.cviu.2016.03.013

    Article  Google Scholar 

  21. Rokach L, Maimon O (2005) Top-down induction of decision trees classifiers—a survey. IEEE Trans Syst. Man Cybern. Part C Appl. Rev. 35:476–487. https://doi.org/10.1109/TSMCC.2004.843247

    Article  Google Scholar 

  22. McNabb P, Wilson D, Bialek J (2013) Classification of mode damping and amplitude in power systems using synchrophasor measurements and classification trees. IEEE Trans Power Syst 28:1988–1996. https://doi.org/10.1109/TPWRS.2013.2240022

    Article  Google Scholar 

  23. Binary decision tree for classification—MATLAB—MathWorks India, MathWorks. (2019). https://in.mathworks.com/help/stats/classificationtree-class.html. Accessed June 9, 2019

  24. ImageJ, (n.d.). https://imagej.net/Welcome. Accessed January 2, 2020

  25. Kudo K (1998) Fractal analysis of electrical trees. IEEE Trans Dielectr Electr Insul 5:713–727. https://doi.org/10.1109/94.729694

    Article  Google Scholar 

  26. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874. https://doi.org/10.1016/j.patrec.2005.10.010

    Article  Google Scholar 

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Vidya, M.S., Sunitha, K., Ashok, S. et al. A model based on bag of visual words to predict the category of damage in XLPE insulation under the application of combined AC and repeated lightning impulses of both polarities. Electr Eng 103, 2825–2836 (2021). https://doi.org/10.1007/s00202-021-01269-7

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