Study on Deep-Learning Model for Phase Resolved Partial Discharge Pattern Classification Based on Convolutional Neural Network Algorithm
Author:
Funder
Korea Institute of Energy Technology Evaluation and Planning
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42835-024-01967-9.pdf
Reference9 articles.
1. Maheswari RV, Perumal S, Vigneshwaran B, Mariasiluvairaj WI (2014) Partial discharge signal denoising using adaptive translation invariant wavelet transform-online measurement. J Electr Eng Technol 9(2):695–706. https://doi.org/10.5370/JEET.2014.9.2.695
2. Hosseini SMH, Baravati PR (2015) Partial discharge localization based on detailed models of transformer and wavelet transform techniques. J Electr Eng Technol 10(3):1093–1101. https://doi.org/10.5370/JEET.2015.10.3.1093
3. Arvind Shriram RK, Chandrasekar S, Karthik B (2018) PD Signal time-frequency map and PRPD pattern analysis of nano SiO₂ modified palm oil for transformer insulation applications. J Electr Eng Technol 13(2):902–910. https://doi.org/10.5370/JEET.2018.13.2.902
4. Li J, Han X, Liu Z, Yao X (2016) A novel GIS partial discharge detection sensor with integrated optical and UHF methods. IEEE Trans Power Deliv 33(4):2047–2049. https://doi.org/10.1109/TPWRD.2016.2635382
5. Hudon C, Belec M (2005) Partial discharge signal interpretation for generator diagnostics. IEEE Trans Dielectr Electr Insul 12(2):297–319. https://doi.org/10.1109/TDEI.2005.1430399
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