• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
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.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2026-10 (Vol.75 No.10)
  4. 10.5370/KIEE.2026.75.10.2495
XML PDF INFO REF

References

1 
Korea Electric Power Corporation, "Statistics of Electric Power in Korea (2025)," no. 95, 2026. Google Search
2 
"IEEE Guide for Loading Mineral-Oil-Immersed Transformers and Step-Voltage Regulators, IEEE Std C57.91-2011," IEEE, 2012. DOI
3 
"Power transformers - Part 7: Loading guide for mineral-oil-immersed power transformers, IEC 60076-7:2018, 2nd ed.," International Electrotechnical Commission, 2018. Google Search
4 
D. H. Shroff, A. W. Stannett, "A review of paper aging in power transformers," IEE Proceedings C - Generation, Transmission and Distribution, vol. 132, no. 6, pp. 312-319, 1985. DOI
5 
"Measurement of the average viscometric degree of polymerization of new and aged cellulosic electrically insulating materials, IEC 60450:2004+ AMD1:2007, ed. 2.1," International Electrotechnical Commission, 2007. Google Search
6 
O. Lee, H. Lee, C. Cho, S.-B. Kim, K.-S. Kim, "Analysis of Asset Status by Operation and Power outage data of Distribution Transformers," pp. 1729-1730, Proc. KIEE Summer Conf., 2020. Google Search
7 
A. M. Emsley, R. J. Heywood, M. Ali, C. M. Eley, "On the kinetics of degradation of cellulose," Cellulose, vol. 4, no. 1, pp. 1-5, 1997. DOI
8 
N. Lelekakis, D. Martin, J. Wijaya, "Ageing rate of paper insulation used in power transformers, Part 1: Oil/paper system with low oxygen concentration," IEEE Trans. Dielectr. Electr. Insul., vol. 19, no. 6, pp. 1999-2008, 2012. DOI
9 
S. S. M. Ghoneim, M. Baz, A. Alzaed, Y. T. Zewdie, "Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models," Scientific Reports, vol. 15, 2025. DOI
10 
H. Jung, J. C. Lee, J. H. Ahn, "A Study on Deep Learning Algorithms for Temperature Prediction of Oil-Immersed Transformers," pp. 2554-2555, Proc. KIEE Summer Conf., 2026. Google Search
11 
L. E. Lundgaard, W. Hansen, D. Linhjell, T. J. Painter, "Aging of oil-impregnated paper in power transformers," IEEE Trans. Power Delivery, vol. 19, no. 1, pp. 230-239, 2004. DOI
12 
M. Raissi, P. Perdikaris, G. E. Karniadakis, "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations," J. Comput. Phys., vol. 378, pp. 686-707, 2019. DOI
13 
S. Hochreiter, J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997. DOI