Application of Empirical Mode Decomposition and Support Vector Machine for the Classification of Arc Fault in Distribution Line-Reference-Cited by-同舟云学术

Application of Empirical Mode Decomposition and Support Vector Machine for the Classification of Arc Fault in Distribution Line

Published:2021 Issue: Volume: Page:385-393
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Lala Himadri,Karmakar Subrata

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-8752-8_39

Reference24 articles.

1. Tengdin, J., Westfall, R., & Stephan, K. (1996). High Impedance Fault Detection Technology (pp. 1–4).

2. Aucoin, B. M., & Jones, R. H. (1996). High impedance fault detection implementation issues. IEEE Trans. Power Deliv., 11(1), 139–148.

3. Chen, J. C., Phung, B. T., Zhang, D. M., Blackburn, T., & Ambikairajah, E. (2013). Study on high impedance fault arcing current characteristics. Australasian Universities Power Engineering Conference (AUPEC), 2013, 1–6.

4. Chen, J., Phung, T., Blackburn, T., Ambikairajah, E., & Zhang, D. (2016). Detection of high impedance faults using current transformers for sensing and identification based on features extracted using wavelet transform. Transm. Distrib. IET Gener., 10(12), 2990–2998.

5. Ghaderi, A., Ginn, H. L., & Mohammadpour, H. A. (2017). High impedance fault detection: A review. Electric Power Systems Research, 143, 376–388.

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