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The classification model for identifying single-phase earth ground faults in the distribution network jointly driven by physical model and machine learning

Overview of attention for article published in Frontiers in Energy Research, January 2023
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About this Attention Score

  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
2 X users

Citations

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1 Dimensions

Readers on

mendeley
2 Mendeley
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Title
The classification model for identifying single-phase earth ground faults in the distribution network jointly driven by physical model and machine learning
Published in
Frontiers in Energy Research, January 2023
DOI 10.3389/fenrg.2022.919041
Authors

Su Xueneng, Zhang Hua, Gao Yiwen, Huang Yan, Long Cheng, Li Shilong, Zhang Weiwei, Zheng Qin

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 50%
Researcher 1 50%
Readers by discipline Count As %
Engineering 2 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 January 2023.
All research outputs
#19,021,638
of 23,578,918 outputs
Outputs from Frontiers in Energy Research
#798
of 3,620 outputs
Outputs of similar age
#300,544
of 438,031 outputs
Outputs of similar age from Frontiers in Energy Research
#24
of 385 outputs
Altmetric has tracked 23,578,918 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,620 research outputs from this source. They receive a mean Attention Score of 1.7. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 438,031 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 385 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.