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Impulse signals classification using one dimensional convolutional neural network


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[1] M.-T. Nguyen, V.-H. Nguyen, S.-J. Yun, and Y.-H. Kim, “Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear”, Energies, vol. 11, no. 5, p. 1202, 2018.10.3390/en11051202Search in Google Scholar

[2] H. H. Sinaga, T. R. Blackburn, and B. Phung, “Recognition of single and multiple partial discharge sources in transformers based on ultra-high frequency signals”, IET Generation, Transmission & Distribution, vol. 8, no. 1, pp. 160-169, 2014.10.1049/iet-gtd.2013.0131Search in Google Scholar

[3] M. Y. A. Khan and J.-Y. Koo, “Neural network-based diagnosis of partial discharge defects patterns at XLPE cable under DC stress”, Electrical Engineering, vol. 99, no. 1, pp. 119-132, 2016.10.1007/s00202-016-0395-0Search in Google Scholar

[4] M. Quizhpi-Cuesta, F. Gomez-Juca, W. Orozco-Tupacyupanqui, and F. Quizhpi-Palomeque, “Classification of partial discharge in pin type insulators using fingerprints and neural networks”, 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2017.10.1109/ROPEC.2017.8261653Search in Google Scholar

[5] Schober and U. Schichler, “Application of Machine Learning for Partial Discharge Classification under DC Voltage”, Proceedings of the Nordic Insulation Symposium, no. 26, pp. 16-21, 2019.10.5324/nordis.v0i26.3268Search in Google Scholar

[6] W. J. K. Raymond, H. A. Illias, A. H. A. Bakar, and H. Mokhlis, “Partial discharge classifications: Review of recent progress”, Measurement, vol. 68, pp. 164-181, 2015.10.1016/j.measurement.2015.02.032Search in Google Scholar

[7] B. Moons, D. Bankman, and M. Verhelst “”,, Embedded Deep Learning, Springer, 2019.10.1007/978-3-319-99223-5Search in Google Scholar

[8] A. Kúchler, High Voltage Engineering: Fundamentals-Technology-Applications, Springer, 2018.Search in Google Scholar

[9] D. Issa, M. F. Demirci, and A. Yazici, “Speech emotion recognition with deep convolutional neural networks”, Biomedical Signal Processing and Control, vol. 59, p. 101894, 2020.Search in Google Scholar

[10] Li, X. Wang, X. Li, A. Yang, and M. Rong, “Partial Discharge Recognition with a Multi-Resolution Convolutional Neural Network”, Sensors, vol. 18, no. 10, p. 3512, 2018.10.3390/s18103512621074230340354Search in Google Scholar

[11] B. T. Phung, Z. Liu, T. R. Blackburn, and R. E. James, Recent Developments for On-line Partial Discharge Detection in Cables, School of Electrical Engineering and Telecommunications University of New South Wales, Australia, 2001.Search in Google Scholar

[12] F. Chollet, Keras, 2015, https://github.com/fchollet/keras.Search in Google Scholar

[13] Gibson and J. Patterson, Deep learning: a practitioner approach, O Reilly, 2017.Search in Google Scholar

[14] L. Woon, Z. Aung, and A. El-Hag, “Intelligent Monitoring of Transformer Insulation Using Convolutional Neural Networks”, Data Analytics for Renewable Energy Integration, Technologies, Systems and Society Lecture Notes in Computer Science, pp. 127-136, 2018.10.1007/978-3-030-04303-2_10Search in Google Scholar

[15] U. Fromm, “Interpretation of partial discharges at dc voltages”, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 2, no. 5, pp. 761-770, 1995.10.1109/94.469972Search in Google Scholar

[16] V Peak signal detection in realtime time series data, https://stackoverflow.com/questions/22583391/peak-signal-detection-in-realtime-xseries-data/54507329.Search in Google Scholar

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