KIEE - The Transactions of the Korean Institute of Electrical Engineers

1

H. Liang, X. Han, H. Yu, F. Li, Z. Liu, and K. Zhang, “Transmission line fault-cause identification method for large-scale new energy grid connection scenarios,” Global Energy Interconnection, vol. 5, no. 4, pp. 362-374, 2022.

2

A. Yadav, and Y. Dash, “An overview of transmission line protection by artificial neural network: fault detection, fault classification, fault location, and fault direction discrimination,” Advances in Artificial Neural Systems, vol. 2014, no. 1, Article 230382, 2014.

3

R. Kuffel, J. Giesbrecht, T. Maguire, R.P. Wierchx, and P. Melaren, “RTDS-A Fully Digital Power System Simulator Operation in Real Time,” in Proc. 1995 International Conference on Energy Management and Power Delivery EMPD'95, vol. 2, pp. 19-24, 1995.

4

A. Abdullah, “Ultrafast transmission line fault detection using a DWT-based ANN,” IEEE Transactions on Industry Applications, vol. 54, no. 2, pp. 1182-1193, 2017.

5

Y. Xing, Y. Liu, D. Lu, X. Zou, and X. He, “A Physics-Informed Data-Driven Fault Location Method for Transmission Lines Using Single-Ended Measurements with Field Data Validation,” arXiv preprint arXiv:2307.09740, 2023.

6

L. Yang, Z. Wang, S. Wang, F. Deng, and P. Yang, “Simulation on HVDC Actual Fault and Analysis of Simulation Reliability,” in Proc. 2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES), IEEE, pp. 789-793, 2021.

7

T. G. Kim, S. H. Lim, K. M. Song, and S. Yoon, “LSTM-based Fault Classification Model in Transmission Lines for Real Fault Data,” KIEE Journal, vol. 73, no. 3, pp. 585-592, 2024.

8

M. Pazoki, “A New Fault Classifier in Transmission Lines Using Intrinsic Time Decomposition,” IEEE Transactions on Industrial Informatics, vol. 14, no. 2, pp. 619-628, 2018.

9

A. Mukherjee, P. K. Kundu, and A. Das, “Transmission line fault classification under high noise in signal: a direct PCA-threshold-based approach,” Journal of The Institution of Engineers (India): Series B, vol. 103, pp. 197-211, 2021.

10

A. Mukherjee, P. K. Kundu, and A. Das, “A differential signal-based fault classification scheme using PCA for long transmission lines,” Journal of The Institution of Engineers (India): Series B, vol. 102, pp. 403-414, 2021.

11

M. F. Guo, W. L. Liu, J. H. Gao, and D. Y. Chen, “A data-enhanced high impedance fault detection method under imbalanced sample scenarios in distribution networks,” IEEE Transactions on Industry Applications, vol. 59, no. 4, pp. 4720-4733, 2023.

12

W. L. Liu, M. F. Guo, and J. H. Gao, “High impedance fault diagnosis method based on conditional Wasserstein generative adversarial network,” in Proc. 2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE), IEEE, pp. 1-6, 2021.

13

A. Torfi, M. Beyki, and E. A. Fox, “On the evaluation of generative adversarial networks by discriminative models,” in 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, pp. 991-998, 2021.

14

M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in Neural Information Processing Systems, vol. 30, 2017.

15

S. Barratt, and R. Sharma, “A note on the inception score,” arXiv preprint arXiv:1801.01973, 2018.

16

K. Y. Lee, S. H. Lim, T. G. Kim, K. M. Song, S. G. Yoon, "GAN-based Generative Algorithm for Various Transmission Line Fault Data," in Proc. KIEE spring conference, 2024.

17

X. Gao, F. Deng, and X. Yue, “Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty,” Neurocomputing, vol. 396, pp. 487-494, 2020.

18

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, and Y. Bengio, “Generative adversarial nets,” Advances in Neural Information Processing Systems, vol. 27, 2014.

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I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” Advances in Neural Information Processing Systems, vol. 30, 2017.

20

F. T. Liu, K. M. Ting, and Z. H. Zhou, “Isolation forest,” in 2008 Eighth IEEE International Conference on Data Mining, IEEE, pp. 413-422, 2008.

21

K. M. Song, T. G. Kim, C. W. Park, and S. G. Yoon, “Transmission Line Fault Cause Modeling and Waveform Analysis,” in Proc. KIEE spring conference, 2023.

22

L. Van der Maaten, and G. Hinton, “Visualizing data using t-SNE,” Journal of machine learning research, vol. 9, no. 11, pp. 2579-2605, 2008.

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers