Comparative Investigation and Determination of Partial Discharge Source Using Self-organizing Map and K-nearest Neighbor Methods of Artificial Neural Network-Reference-Cited by-同舟云学术

Comparative Investigation and Determination of Partial Discharge Source Using Self-organizing Map and K-nearest Neighbor Methods of Artificial Neural Network

Published:2022 Issue: Volume: Page:185-194
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Chaudhari Kajol,Kothoke Priyanka,Mishra Akhilesh Kumar,Chaudhari Yogesh

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-2828-4_17

Reference12 articles.

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2. Hirata A, Nakata S, Kawasaki Z-I (2006) Toward automatic classification of partial discharge sources with neural networks. IEEE Trans Power Deliv 21(1):526–527. https://doi.org/10.1109/TPWRD.2005.848439

3. Hyde JR, Kemp IJ (1994) Partial discharge pattern recognition and associated technologies. In: IEE colloquium on monitoring technologies for plant insulation, pp 8/1–8/5

4. Park SH, Lee KW, Lim KJ, Kang SH (2003)Classification of external and internal PD signals generated in molded transformer by neural networks. In: Proceedings of the 7th international conference on properties and applications of dielectric materials (Cat. No.03CH37417), vol 1, pp 463–466. https://doi.org/10.1109/ICPADM.2003.1218451

5. Ping S, Dake X, Guoli W, Yanming L (2002)Application of neural network with genetic algorithm to UHF PD pattern recognition in transformers. In: Annual report conference on electrical insulation and dielectric phenomena, pp 732–735. https://doi.org/10.1109/CEIDP.2002.1048900