KIEE
The Transactions of
the Korean Institute of Electrical Engineers
KIEE
Contact
Open Access
Monthly
ISSN : 1975-8359 (Print)
ISSN : 2287-4364 (Online)
http://www.tkiee.org/kiee
Mobile QR Code
The Transactions of the Korean Institute of Electrical Engineers
ISO Journal Title
Trans. Korean. Inst. Elect. Eng.
Main Menu
Main Menu
최근호
Current Issue
저널소개
About Journal
논문집
Journal Archive
편집위원회
Editorial Board
윤리강령
Ethics Code
논문투고안내
Instructions to Authors
연락처
Contact Info
논문투고·심사
Submission & Review
Journal Search
Home
Archive
2025-11
(Vol.74 No.11)
10.5370/KIEE.2025.74.11.1757
Journal XML
XML
PDF
INFO
REF
References
1
J. Han and A. Papavasiliou, “The impacts of transmission topology control on the European electricity network,” IEEE Transactions on Power Systems, vol. 31, no. 1, pp. 496-507, 2016. DOI:10.1109/TPWRS.2015.2408439
2
J. H. Kim, B. S. Lee and C. H. Kim, “A study on the development of machine-learning based load transfer detection algorithm for distribution planning,” Energies, vol. 13, no. 17, Art. 4358, 2020. DOI:10.3390/en13174358
3
M. Sobhani, P. Wang and T. Hong, “Detecting load transfers,” IEEE Transactions on Smart Grid, vol. 14, no. 2, pp. 1367-1375, 2023. DOI:10.1109/TSG.2022.3191734
4
X. Wang and S.H. Ahn, “Real-time prediction and anomaly detection of electrical load in a residential community,” Applied Energy, vol. 259, Art. 114145, 2020. DOI:10.1016/j.apenergy.2019.114145
5
J. Zhang, H. Zhang, S. Ding and X. Zhang, “Power consumption predicting and anomaly detection based on Transformer and K-Means,” Frontiers in Energy Research, vol. 9, Art. 779587, 2021. DOI:10.3389/fenrg.2021.779587
6
M. Karkhaneh and S. Ozgoli, “Anomalous load profile detection in power systems using wavelet transform and robust regression,” Advanced Engineering Informatics, vol. 53, Art. 101639, 2022. DOI:10.1016/j.aei.2022.101639
7
M. Choubey, R. K. Chaurasiya and J. S. Yadav, “Contrastive learning for efficient anomaly detection in electricity load data,” Sustainable Energy, Grids and Networks, vol. 42, Art. 101639, 2025. DOI:10.1016/j.segan.2025.101639
8
N. Xiao, “Review of detection methods for abnormal electricity consumption data in smart grid,” International Journal of Computer Science and Information Technology, vol. 3, no. 1, pp. 63-72, 2024. DOI:10.62051/ijcsit.v3n1.10
9
J. Duan, “Deep learning anomaly detection in AI-powered intelligent power distribution systems,” Frontiers in Energy Research, vol. 12, Art. 1364456, 2024. DOI:10.3389/fenrg.2024.1364456
10
R. Lin, S. Chen, Z. He, B. Wu, H. Zou, X. Zhao and Q. Li, “Electricity behavior modeling and anomaly detection services based on a deep variational autoencoder network,” Energies, vol. 17, no. 16, Art. 3904, 2024. DOI:10.3390/en17163904
11
S. M. Miraftabzadeh, M. Longo, S. Leva and N. Matera, “Data anomaly detection in photovoltaic power time-series via unsupervised deep learning with insufficient information,” Sustainable Energy, Grids and Networks, vol. 43, Art. 101769, 2025. DOI:10.1016/j.segan.2025.101769
12
A. L. Amutha, R. A. Uthra, J. P. Roselyn and R. G. Brunet, “Anomaly detection in multivariate streaming PMU data using density estimation technique in wide-area monitoring system,” Expert Systems with Applications, vol. 175, Art. 114865, 2021. DOI:10.1016/j.eswa.2021.114865
13
M. Dey, S. P. Rana, C. V. Simmons and S. Dudley, “Solar farm voltage anomaly detection using high-resolution PMU data-driven unsupervised machine learning,” Applied Energy, vol. 303, Art. 117656, 2021. DOI:10.1016/j.apenergy.2021.117656
14
M. M. Lakouraj, M. Gautam, H. Livani and M. Benidris, “A multi-rate sampling PMU-based event classification in active distribution grids with spectral graph neural network,” Electric Power Systems Research, vol. 221, Art. 108145, 2022. DOI:10.1016/j.epsr.2022.108145
15
I. Niazazari and H. Livani, “A PMU-data-driven disruptive event classification in distribution systems,” Electric Power Systems Research, vol. 157, pp. 251-260, 2018. DOI:10.1016/j.epsr.2017.12.021
16
X. Wang, Z. Yao and M. Papaefthymiou, “A real-time electrical load forecasting and unsupervised anomaly detection framework,” Applied Energy, vol. 330, Art. 120279, 2023. DOI:10.1016/j.apenergy.2022.120279
17
R. Vaish, U. D. Dwivedi, S. Tewari and S. M. Tripathi, “Machine learning applications in power system fault diagnosis: Research advancements and perspectives,” Engineering Applications of Artificial Intelligence, vol. 106, Art. 104504, 2021. DOI:10.1016/j.engappai.2021.104504
18
R. Pourramezan, H. Karimi and J. Mahseredjian, “Synchrophasor network-based detection and classification of power system events: A singular value decomposition approach,” Electric Power Systems Research, vol. 223, Art. 109645, 2023. DOI:10.1016/j.epsr.2023.109645
19
Y. Taleb and E. A. K. Cohen, “Multiresolution analysis of point processes and statistical thresholding for Haar wavelet-based intensity estimation,” Annals of the Institute of Statistical Mathematics, vol. 73, no. 3, pp. 395-423, 2021. DOI:10.1007/s10463-020-00753-4
20
A. Ukil and R. Živanović, “Adjusted Haar wavelet for application in power systems disturbance analysis,” Digital Signal Processing, vol. 18, no. 2, pp. 103-115, 2008. DOI:10.1016/j.dsp.2007.04.001
21
M. Aqil and A. Jbari, “Electrocardiogram features detection using stationary wavelet transform,” International Journal of Electrical and Computer Engineering, vol. 15, no. 1, pp. 374-385, 2025. DOI:10.11591/ijece.v15i1.pp374-385
22
H. Wang, F. Zhou, C. Jiang, L. Qin and H. Zhang, “Change point detection for piecewise envelope current signal based on wavelet transform,” Journal of Electrical and Computer Engineering, vol. 2018, Art. 9529870, 2018. DOI:10.1155/2018/9529870
23
F. T. Liu, K. M. Ting and Z.-H. Zhou, “Isolation forest,” Proceedings of the IEEE International Conference on Data Mining (ICDM), Pisa, Italy, pp. 413-422, 2008. DOI:10.1109/ICDM.2008.17