Deep learning for high-impedance fault detection and classification: transformer-CNN
Author:
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07219-z.pdf
Reference35 articles.
1. Wang B, Geng J, Dong X (2018) High-impedance fault detection based on nonlinear voltage-current characteristic profile identification. IEEE Trans Smart Grid 9(4):3783–3791
2. Gautam S, Brahma SM (2013) Detection of high impedance fault in power distribution systems using mathematical morphology. IEEE Trans Power Syst 28(2):1226–1234
3. Wei M, Liu W, Zhang H, Shi F, Chen W (2021) Distortion-based detection of high impedance fault in distribution systems. IEEE Trans Power Deliv 36(3):1603–1618
4. Yeh H-G, Sim S, Bravo RJ (2019) Wavelet and denoising techniques for real-time HIF detection in 12-kv distribution circuits. IEEE Syst J 13(4):4365–4373
5. Wang S, Dehghanian P (2020) On the use of artificial intelligence for high impedance fault detection and electrical safety. IEEE Trans Ind Appl 56(6):7208–7216
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