• 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. 2026-11 (Vol.75 No.11)
  4. 10.5370/KIEE.2026.75.11.3099
XML PDF INFO REF

References

1 
Z. Chen, A. Moradi Amani, X. Yu, M. Jalili, "Control and Optimisation of Power Grids Using Smart Meter Data: A Review," Sensors, vol. 23, no. 4, 2023. DOI
2 
M. Alenezi, F. Anayi, M. Packianather, M. Shouran, "Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection," Sustainability, vol. 16, no. 23, 2024. DOI
3 
H. Kim, K. Song, D. Kim, C. Choi, "A Study on the Determination of Burnout Causes of Metering Out Fit (MOF) Installed in 22.9kV Receiving Systems," J. Korean Institute of Fire Science & Engineering, vol. 19, no. 1, pp. 93-98, 2005. Google Search
4 
D. Shin, I. Song, J. Park, E. Bae, J. Shin, "Introduction to Live-Line Ratio Error Measurement Technology for Metering Out Fit (MOF)," pp. 1972-1973, Proc. KIEE Summer Conf., 2019. Google Search
5 
J. Park, "A Study on Three-Phase Integrated MOF PT Fault Detection Using Secondary Voltage and Current," pp. 645-646, Proc. KIEE Summer Conf., 2021. Google Search
6 
K. Gandhi, P. Verma, "Anomaly Detection in AMI and Smart Meter Data for Electricity Theft, Outage, and Equipment Fault Identification: A Comprehensive Review," Int. J. Emerging Trends in Computer Science and Information Technology, vol. 4, no. 3, pp. 189-197, 2023. DOI
7 
E. Altamimi, A. Al-Ali, A. K. Al-Ali, Q. M. Malluhi, "Distribution-Based Residual Analysis of Load Forecasting for Enhanced Anomaly Detection," Int. J. Electrical Power & Energy Systems, vol. 173, 2025. DOI
8 
J. Lee, D. Kwon, Y. Ji, "Electric Power Time Series Data-Based Unsupervised Anomaly Detection Method for Equipment," Korean Institute of Communications and Information Sciences, vol. 49, no. 7, pp. 993-1001, 2024. DOI
9 
F. M. Dahunsi, A. E. Olawumi, D. T. Ale, O. A. Sarumi, "A Systematic Review of Data Pre-processing Methods and Unsupervised Mining Methods Used in Profiling Smart Meter Data," AIMS Electronics and Electrical Engineering, vol. 5, no. 4, pp. 284-314, 2021. DOI
10 
D. Kim, A. Kareem, D. Domingo, B. Shin, J. Hur, "Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps," J. Sens. Actuator Netw., vol. 13, no. 5, 2024. DOI
11 
N. Chawla, K. Bowyer, L. Hall, W. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," J. Artificial Intelligence Research, vol. 16, pp. 321-357, 2002. DOI
12 
S. Szeghalmy, A. Fazekas, "A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning," Sensors, vol. 23, no. 4, 2023. DOI
13 
Z. C. Lipton, C. Elkan, B. Narayanaswamy, "Thresholding Classifiers to Maximize F1 Score," arXiv preprint arXiv:1402.1892, 2014. DOI
14 
P. J. Rousseeuw, K. Van Driessen, "A fast algorithm for the minimum covariance determinant estimator," Technometrics, vol. 41, no. 3, pp. 212-223, 1999. DOI
15 
J. Hardin, D. M. Rocke, "The Distribution of Robust Distances," J. Computational and Graphical Statistics, vol. 14, no. 4, pp. 928-946, 2005. DOI
16 
D. Bank, N. Koenigstein, R. Giryes, "Autoencoders," Machine Learning for Data Science Handbook, Springer International Publishing, pp. 353-374, 2023. DOI
17 
L. Breiman, "Random Forests," Machine Learning, vol. 45, pp. 5-32, 2001. DOI
18 
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T.-Y. Liu, "LightGBM: A Highly Efficient Gradient Boosting Decision Tree," Advances in Neural Information Processing Systems, vol. 30, pp. 3146-3154, 2017. Google Search
19 
T. Chen, C. Guestrin, "XGBoost: A Scalable Tree Boosting System," pp. 785-794, Proc. 22nd ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2016. DOI