Deep Learning Berbasis CNN Untuk Pengenalan Pola Partial Discharge Isolasi Silicone Rubber

Ferlian  Seftianto; Sukemi Sukemi; Zainuddin  Nawawi

doi:10.31598/sintechjournal.v6i2.1390

Authors

Ferlian Seftianto Universitas Sriwijaya
Sukemi Sukemi Universitas Sriwijaya
Zainuddin Nawawi Universitas Sriwijaya

DOI:

https://doi.org/10.31598/sintechjournal.v6i2.1390

Keywords:

partial discharge, de-noising, pattern recognition, CNN, SVM

Abstract

Partial discharge (PD) activity measurements have been carried out by selecting noise signals (de-noising) using Support Vector Machine (SVM)and then recognized using Convolutional Neural Network (CNN). CNN testing was carried out using various models such as activation methods: Sigmoid, Softmax, Relu, Tanh, Swish. Number of layers used is 1, 2, 3, 4 with filter sizes of 32, 64, 128, 256 and kernel sizes 3x3, 2x2, 1x1, 1x2, 1x3 in the MaxPooling and AveragePooling pooling methods. The results obtained, On sigmoid method the MaxPooling and AveragePooling with 1 layers having a low accuracy around 14.40% but the other layers configurations gets a high accuracy around 98.99% both has been done with or without de-noising. In Softmax activation method, MaxPooling pooling method has an accuracy around 84.94% and has de-noising 90.66%. The AveragePooling pooling method has an accuracy 65.25% and around 75.29% with de-noised. The result shows that SVM de-noising increases the accuracy around 11.12% in the Softmax activation method. In the Tanh, Relu, and Swish activation methods, a low level of accuracy is obtained with an average of 14.40%, and SVM de-noising doesn’t increase the accuracy, so CNN-based deep learning with SVM de-noising is more suitable using the Sigmoid and Softmax.

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References

M. T. Nazir, B. T. Phung, and M. Hoffman, “Performance of silicone rubber composites with SiO2 micro/nano-filler under AC corona discharge,” IEEE Trans. Dielectr. Electr. Insul., vol. 23, no. 5, pp. 2804–2815, 2016, doi: 10.1109/TDEI.2016.7736840.

M. S. Naidu and V. Kamaraju, High Voltage Engineering. Tata McGraw-Hill, 2004.

C. L. Wadhwa, High Voltage Engineering. New Age International (P) Limited, 2006.

E. Kuffel and W. S. Zaengl, High Voltage Engineering Fundamentals, 2nd ed. 2000.

Q. M. . M. N. H. Al-Arainy. A.A, Electrical Insulation in Power System. 2018.

A. J. Pansini, “Power Transmission and Distribution,” in Power Transmission and Distribution, 2005, pp. i–xiv.

R. Arora and W. Mosch, High voltage and electrical insulation engineering. 2011.

Suwarno, “Partial discharges in high voltage insulations: Mechanism, patterns and diagnosis,” Proc. 2014 Int. Conf. Electr. Eng. Comput. Sci. ICEECS 2014, no. November, pp. 369–375, 2014, doi: 10.1109/ICEECS.2014.7045280.

N. A. Awang et al., “Partial Discharge and Breakdown Strength of Plasma Treated Nanosilica/LDPE Nanocomposites,” in 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2018, pp. 391–394, doi: 10.1109/EECSI.2018.8752717.

W. R. Putra, I. M. Y. Negara, and I. Satriyadi, “Pengaruh Bentuk dan Material Elektrode terhadap Partial Discharge,” J. Tek. ITS, vol. 4, no. 1, 2015.

N. A. Awang et al., “Effect of humidity on partial discharge characteristics of epoxy/boron nitride nanocomposite under high voltage stress,” Int. J. Electr. Comput. Eng., vol. 7, no. 3, pp. 1562–1567, 2017, doi: 10.11591/ijece.v7i3.pp1562-1567.

H. B. H. Sitorus, H. H. Sinaga, and M. Jaenussolihin, “Pola Peluahan Parsial Pada Bahan Isolasi Epoxy Resin,” Electr. J. Rekayasa dan Teknol. Elektro UNILA, vol. 2, no. 2, 2008.

R. Arora and W. Mosch, “Solid Dielectrics, their Sources, Properties, and Behavior in Electric Fields,” in High Voltage and Electrical Insulation Engineering, 2011, pp. 319–370.

X. Peng et al., “A Convolutional Neural Network-Based Deep Learning Methodology for Recognition of Partial Discharge Patterns from High-Voltage Cables,” IEEE Trans. Power Deliv., vol. 34, no. 4, pp. 1460–1469, 2019, doi: 10.1109/TPWRD.2019.2906086.

H. Zhang, X. Xu, Y. Yan, P. Xu, Y. Lu, and Z. Hou, “Recognition of Partial Discharge in Switchgear Based on Kohonen Network,” 2020 IEEE Electr. Insul. Conf. EIC 2020, pp. 542–545, 2020, doi: 10.1109/EIC47619.2020.9158688.

S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning From Theory to Algorithms. New York: Cambridge University Press, 2014.

H. Niu, A. Cavallini, G. C. Montanari, and Y. Zhang, “Noise rejection strategy and experimental research on partial discharge at DC voltage,” in 2009 IEEE 9th International Conference on the Properties and Applications of Dielectric Materials, 2009, pp. 489–492, doi: 10.1109/ICPADM.2009.5252385.

C. Ma et al., “Background Noise of Partial Discharge Detection and Its Suppression in Complex Electromagnetic Environment,” in 2018 IEEE International Conference on High Voltage Engineering and Application (ICHVE), 2018, pp. 1–4, doi: 10.1109/ICHVE.2018.8642084.

H. Zhou, D. Wan, M. Zhao, J. Fang, W. Zhou, and S. Peng, “Signal Recognition Method of Power Cable Oscillating Wave Partial Discharge Detection Based on Support Vector Machine,” in 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), 2019, pp. 2334–2337, doi: 10.1109/EI247390.2019.9061706.

S. Abe, Support Vector Machines for Pattern Classification. Springer London, 2010.