Pattern Recognition of Partial Discharge in Power Transformer Based on InfoGAN and CNN
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42835-022-01260-7.pdf
Reference41 articles.
1. Xi YuY, Chen L, Chen B et al (2022) Research on pattern recognition method of transformer partial discharge based on artificial neural network. Secur Commun Netw. https://doi.org/10.1155/2022/5154649
2. Kim Y, Park T, Kim S et al (2019) Artificial intelligent fault diagnostic method for power transformers using a new classification system of faults. J Electr Eng Technol 14:825–831. https://doi.org/10.1007/s42835-019-00105-0
3. Zhang XR, Wang HT, Guo RC et al (2022) Fault diagnosis technologies for power transformers during the on-site inductive oscillating switching impulse voltage withstand test. IET Gener Transm Distrib. https://doi.org/10.1049/gtd2.12572
4. Kang A, Tian M, Song J et al (2019) Contribution of electrical-thermal aging to slot partial discharge properties of HV motor windings. J Electr Eng Technol 14:1287–1297. https://doi.org/10.1007/s42835-018-00076-8
5. Khan MA, Choo J, Kim YH (2019) End-to-end partial discharge detection in power cables via time-domain convolutional neural networks. J Electr Eng Technol 14:1299–1309. https://doi.org/10.1007/s42835-019-00115-y
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