• 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. 2023-01 (Vol.72 No.01)
  4. 10.5370/KIEE.2023.72.1.46
XML PDF INFO REF

References

1 
S. Y. Lee, 2022, A Study on Strengthening and Promoting the National Carbon-Neutrality Strategy, policy report, pp. 1-144DOI
2 
Korea Power Exchange, 2021 12, 2021 Global Power Technology Trend, report, pp. 1-607DOI
3 
J. B. Kwon, 2022 5, Technical challenges encountered in integration of renewable energy systems in Denmark, Seminar on how to establish re centered grid system for carbon neutralty, pp. 1-42DOI
4 
A. G. Phadke, T. Bi, 2018 7, Phasor measurement units, WAMS, and their applications in protection and control of power systems, Journal of Modern Power Systems and Clean Energy, Vol. 6, No. 4, pp. 619-629DOI
5 
C. W. Park, 2019, Development of Real-time Monitoring, Analysis, and Control System for New and Renewable Power Plants, KEPCO Field Technology Development Project Final Report, pp. 1-451DOI
6 
S. B. Kang, S. H. You, 2019 7, Status of Power System Situation awareness Technology Using AI and Big Data Based on PMU Data, 2019 KIEE Power System Research Group Summer Conference, pp. 440-441DOI
7 
S. B. Kang, B. K. Ko, 2019 9, Development of Classification Model of Power System Fault by Using PMU Big-Data, The Transactions of the Korean Institute of Electrical Engineers, Vol. 68, No. 8, pp. 1079-1084DOI
8 
S. K. Yoon, J. H. Yang, 2020 10, Event Detection Method Based on DCGAN Using D-PMU Data, 2020 KIEE Power System Research Group fall Conference, pp. 377-378DOI
9 
S. C. Nam, B. K. Ko, 2020 11, Development of Power System Situation Recognition Technology Using, The Transactions of the Korean Institute of Electrical Engineers, Vol. 69, No. 11, pp. 1640-1648DOI
10 
S. K. Yoon, J. H. Yang, 2022 4, DCGAN based Event Detection Scheme Using D-PMU Data in Distribution Systems, The Transactions of the Korean Institute of Electrical Engineers, Vol. 71, No. 4, pp. 555-565DOI
11 
D. B. Son, S. R. Kim, 2022 4, A study on Thevenin Impedance estimation method using PMU, 2022 Power System Protection & Automation Research Group Spring Conference, pp. 107-108DOI
12 
K. M. Lee, C. W. Park, 2022 8, Statistical Analysis of Big Data Using Python for Renewable Energy Sources, Journal of the KIIEE, Vol. 36, No. 8, pp. 26-32DOI
13 
N. Kishor, 2017 12, Event Detection and Its Signal Characterization in PMU Data Stream, IEEE Transactions on Industrial Informatics, Vol. 13, No. 6, pp. 3108-3117DOI
14 
I. Niazazari, H. Livani, 2017 7, Disruptive Event Classification using PMU Data in Distribution Networks, 2017 International Journal of Electrical Power & Energy Systems, Vol. 123, pp. 1-5DOI
15 
F. L. Grando, A. E. Lazzaretti, 2019 12, Fault Classification in Power Distribution Systems using PMU Data and Machine Learning, 2019 20th International Conference on Intelligent System Application to Power Systems(ISAP), pp. 1-6DOI
16 
L. Xie, X. Zheng, 2021 2, Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification, 2021 IEEE Open Access Journal of Power and Energy, Vol. 8, pp. 68-76DOI
17 
R. Majumdar, A. Rai, 202112, Impact of Renewable Energy Penetration on PMU Based Grid Event Detection Using Machine Learning Framework, 2021 9th IEEE International Conference on Power Systems (ICPS), pp. 1-6DOI
18 
Z. Li, J. Zhao, 2022 2, A Power System Disturbance Classification Method Robust to PMU Data Quality Issues, IEEE Transactions on Industrial Informatics, Vol. 18, No. 1, pp. 130-142DOI
19 
S. Bodda, A. Thawait, 2022 6, Comparative Analysis of Deep Learning and Machine Learning Techniques for Power System Fault type Classification and Location Prediction, 2022 IEEE International IOT, Vol. electronics and mechatronics conference (iemtronics), pp. 1-9DOI
20 
I. Goolfelllow, Y. Bengio, A. Courville, 2016, Deep Learning (Adaptive Computation and Machine Learning series), MIT Press, pp. 1-800DOI