Evidence map›Paper›PMID 36450824›Full record

ArticleNature communications2022

Fragmentation of outage clusters during the recovery of power distribution grids.

Hao Wu, Xiangyi Meng, Michael M Danziger, Sean P Cornelius, Hui Tian, Albert-László Barabási

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Hao WuState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China.ORCID http://orcid.org/0000-0003-2591-6085
Xiangyi MengCenter for Complex Networks Research, Department of Physics, Northeastern University, Boston, 02115, USA.ORCID http://orcid.org/0000-0001-5184-7648
Michael M DanzigerCenter for Complex Networks Research, Department of Physics, Northeastern University, Boston, 02115, USA.ORCID http://orcid.org/0000-0002-2674-0109
Sean P CorneliusDepartment of Physics, Ryerson University, 350 Victoria Street, M5B 2K3, Toronto, Canada.
Hui TianState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China. tianhui@bupt.edu.cn.ORCID http://orcid.org/0000-0001-8876-1389
Albert-László BarabásiCenter for Complex Networks Research, Department of Physics, Northeastern University, Boston, 02115, USA.ORCID http://orcid.org/0000-0002-4028-3522

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62071068
6 · The paper itself

Abstract

The understanding of recovery processes in power distribution grids is limited by the lack of realistic outage data, especially large-scale blackout datasets. By analyzing data from three electrical companies across the United States, we find that the recovery duration of an outage is connected with the downtime of its nearby outages and blackout intensity (defined as the peak number of outages during a blackout), but is independent of the number of customers affected. We present a cluster-based recovery framework to analytically characterize the dependence between outages, and interpret the dominant role blackout intensity plays in recovery. The recovery of blackouts is not random and has a universal pattern that is independent of the disruption cause, the post-disaster network structure, and the detailed repair strategy. Our study reveals that suppressing blackout intensity is a promising way to speed up restoration.

Identifiers

PMID36450824
PMCPMC9712383

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.