Assisted Diagnosis of Real-Virtual Twin Space for Data Insufficiency-Reference-Cited by-同舟云学术

Assisted Diagnosis of Real-Virtual Twin Space for Data Insufficiency

Published:2021 Issue: Volume: Page:387-395
ISSN:1876-1100
Container-title:The Proceedings of the 9th Frontier Academic Forum of Electrical Engineering
language:
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Author:

Duan Jiajun,He Yigang,Wu Xiaoxin

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-33-6609-1_34

Reference14 articles.

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2. Ruijin, Liao, Wang Youyuan, Liu Hang, et al. 2018. Research status of condition assessment method for power equipment. High Voltage Engineering 44 (11): 3454–3464 (in Chinese).

3. Enwen, Li, Wang Linong, Song Bin, Fang Yaqi. 2019. Parallel clustering analysis of dissolved gas analysis data based on chaotic sequences. Transactions of China Electro-technical Society 34 (24): 5104–5114 (in Chinese).

4. Bo, Qi, Zhang Peng, Rong Zhihai, et al. 2017. Validity assessment method of DGA sensors based on data driven and multiple criterion integration. Power System Technology 41 (11): 3662–3669 (in Chinese).

5. Ozcanli, A.K., F. Yaprakdal, and M. Baysal. 2020. Deep learning methods and applications for electrical power systems: A comprehensive review. International Journal of Energy Research 44 (9): 7136–7157.

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