Image fusion of fault detection in power system based on deep learning-Reference-Cited by-同舟云学术

Image fusion of fault detection in power system based on deep learning

Published:2018-02-28 Issue:S4 Volume:22 Page:9435-9443
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Li Yu,Yu Fengyuan,Cai Qian,Yuan Kun,Wan Renzhuo,Li Xiaoying,Qian Meiyu,Liu Pengfeng,Guo Junwen,Yu Juan,Zheng Tian,Yan Huan,Hou Peng,Feng Yiming,Wang Siyuan,Ding Lei

Funder

National Natural Science Foundation of China

Government Support Enterprise Development Funding of Hubei Province

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

http://link.springer.com/article/10.1007/s10586-018-2264-2/fulltext.html

Reference22 articles.

1. Huang, T., Wu, M., Xie, L.: Prioritization of PMU location and signal selection for monitoring critical power system oscillations. IEEE Trans. Power Syst. (2017). https://doi.org/10.1109/TPWRS.2017.2776103

2. Oubrahim, Z., Choqueuse, V., Amirat, Y., Benbouzid, M.E.H.: Disturbances classification based on a model order selection method for power quality monitoring. IEEE Trans. Ind. Electron. 64(12), 9421–9432 (2017)

3. You, J., Chen, C.C., Hong, Z., et al.: 10.2% power conversion efficiency polymer tandem solar cells consisting of two identical sub-cells. Adv. Mater. 25(29), 3973–3978 (2013)

4. Dobakhshari, Ahmad Salehi, Ranjbar, A.M.: A wide-area scheme for power system fault location incorporating bad data detection. IEEE Trans. Power Delivery 30(2), 800–808 (2015)

5. Dubey, Rahul, Samantaray, S.R.: Wavelet singular entropy-based symmetrical fault-detection and out-of-step protection during power swing. IET Gener. Transm. Distrib. 7(10), 1123–1134 (2013)

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