Evidence map›Paper›PMID 37960647›Full record

ArticleSensors (Basel, Switzerland)2023

Single Line-to-Ground Fault Type Multilevel Classification in Distribution Network Using Realistic Recorded Waveform.

Jiajun Liu, Chenjing Li, Yue Liu, Ji Sun, Haokun Lin

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Jiajun LiuSchool of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.
Chenjing LiSchool of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.ORCID 0009-0001-0287-9886
Yue LiuSchool of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.
Ji SunSchool of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.ORCID 0000-0002-2074-7860
Haokun LinSchool of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.

Funding

National Natural Science Foundation of China 52177114State Grid Xinjiang Electric Power Co., Ltd. D230DK210023
6 · The paper itself

Abstract

The further identification of fault types for single line-to-ground faults (SLGFs) in distribution networks is conducive to determining the cause of grounding faults and formulating targeted measures for hidden danger treatment and fault prevention. For the six types of SLGFs generated in the actual power grid, this paper deeply studies their fault characteristics. Firstly, the classification criterion of fault transition resistance is derived by the generation mechanism of fault zero sequence voltage (ZSV). At the same time, by comparing and analyzing the same and different characteristics between faults, three criteria for fault classification are obtained. Based on the above four criteria, a multilevel and multicriteria fault classification method is proposed to judge six types of SLGFs. Then, the proposed method is verified by various fault state simulations of the distribution network model with a balanced topology and unbalanced topology. The engineering application of the method is demonstrated by the verification of actual power grid data. Finally, noise and data loss interference test results show the robustness of the method.

Indexed as

fault recorder datamultilevel classificationmulti-type faultssingle line-to-ground fault

Identifiers

PMID37960647
PMCPMC10647234

What OpenQuestion holds

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Registered trials

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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.