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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
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References

1 
M. Yulong, Y. Qiang, W. Siying, Z. Fuping, T. Ju, Z. Ning, 2018, A New Method for Fault Diagnosis of SF6In- sulation Equipment Based on Decomposed Components Analysis, 2018 Condition Monitoring and Diagnosis (CMD), Vol. perth, No. wa, pp. 1-4DOI
2 
S. Li, J. Li, 6 2017, Condition monitoring and diagnosis of power equipment: review and prospective, in High Voltage, Vol. 2, No. 2, pp. 82-91DOI
3 
M. Iorgulescu, R. Beloiu, M. O. Popescu, 2010, Vibration monitoring for diagnosis of electrical equipment’s faults, 2010 12th International Conference on Optimization of Electrical and Electronic Equipment, Vol. basov, No. , pp. 493-499DOI
4 
M. Haider, A. Doegar, R. K. Verma, 2018, Fault Identifi- cation in Electrical Equipment using Thermal Image Pro- cessing, 2018 International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, Uttar Pradesh, India, pp. 853-858DOI
5 
A. Bargigia, W. Koltunowicz, A. Pigini, July 1992, Detection of parallel discharges in gas insulated substations, in IEEE Transactions on Power Delivery, Vol. 7, No. 3, pp. 1239-1249DOI
6 
Li et al. Xiuwei, 2012, Partial discharge monitoring system for PD characteristics of typical defects in GIS using UHF method, 2012 International Conference on High Voltage Engineering and Application, Vol. shanghai, pp. 625-628DOI
7 
L. Hao, P. L. Lewin, February 2010, Partial discharge source discri- mination using a support vector machine, in IEEE Tran- sactions on Dielectrics and Electrical Insulation, Vol. 17, No. 1, pp. 189-197DOI
8 
K. J. Park, G. S. Kim, S. K. Oh, W. Choi, J. T. Kim, 200812, A Study on Partial Discharge Pattern Recognition Using Neuro-Fuzzy Techniques, The Transactions of The Korean Institute of Electrical Engineers, Vol. 57, No. 12, pp. 2313-2321Google Search
9 
A. Contin, A. Cavallini, G. C. Montanari, G. Pasini, F. Puletti, June 2002, Digital detection and fuzzy classification of partial discharge signals, in IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 9, No. 3, pp. 335-348DOI
10 
B. Raghavendra, M. Krishna Chaitanya, 2017, Comparative analysis and optimal wavelet selection of partial discharge de-noising methods in Gas-insulated Substation, 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, pp. 1-5DOI
11 
J. Li, T. Jiang, S. Grzybowski, C. Cheng, Dec 2010, Scale dependent wavelet selection for de-noising of partial discharge detection, in IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 17, No. 6, pp. 1705-1714DOI
12 
Q. Zhang, J. Lin, H. Song, G. Sheng, 2018, Fault Identifi- cation Based on PD Ultrasonic Signal Using RNN, DNN and CNN, 2018 Condition Monitoring and Diagnosis (CMD), Vol. perth, No. wa, pp. 1-6DOI
13 
D. H. HUBEL, T. N. WIESEL, 1962, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex., The Journal of physiology, Vol. 160, No. 1 (1962): 106-54DOI
14 
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, , Gradient- based learning applied to document recognition, in Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324DOI
15 
F. -C. Gu, 2020, Identification of Partial Discharge Defects in Gas-Insulated Switchgears by Using a Deep Learning Method, in IEEE AccessDOI
16 
H. Song, J. Dai, G. Sheng, X. Jiang, April 2018, GIS partial discharge pattern recognition via deep convolutional neural network under complex data source, in IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 25, No. 2, pp. 678-685DOI
17 
M. A. Mercioni, S. Holban, 2020, The Most Used Activation Functions: Classic Versus Current, 2020 International Conference on Development and Application Systems (DAS), Vol. suceava, No. Romania, pp. 141-145DOI
18 
N. D. Marom, L. Rokach, A. Shmilovici, 2010, Using the confusion matrix for improving ensemble classifiers, 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, Vol. eliat, pp. 000555-000559DOI