Evidence map›Paper›PMID 40379767›Full record

ArticleScientific reports2025

Research on dynamic modeling and control strategy of motor driven operating mechanism for 126 kV high voltage vacuum circuit breaker.

Yifei Wang, Jianyuan Xu, Wei Li, Jinming Liu

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

4 authors.

Yifei WangSchool of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China. wangyifei407@163.com.
Jianyuan XuSchool of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China.
Wei LiSchool of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China.
Jinming LiuSchool of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China.

Funding

National Natural Science Foundation of China 51777130
6 · The paper itself

Abstract

To mitigate the effects of motor load torque variations on the control performance of a 126 kV high voltage vacuum circuit breaker equipped with a motor driven operating mechanism, this paper presents the derivation of dynamic equations for the motor load torque. These equations, formulated using the Euler-Lagrange equation, Lagrange multipliers, and geometric constraints, are expressed in terms of a single independent variable: the angular displacement of the motor. Utilizing these equations, the load torque can be calculated in real-time using the motor position sensor feedback, and the motor control strategy is optimized through a feedforward method designed to actively compensate for load disturbances. Subsequently, an angular displacement trajectory has been designed to serve as a positional reference for the motor control, with the aim of enhancing the operational reliability of the operating mechanism. Furthermore, comparative control experiments have been conducted in the motor-breaker integrated experimental platform to assess the reduction in the motor's position tracking error, contact bouncing duration, and mechanical collision duration under the optimized motor control strategy. The obtained experimental results reveal the efficacy of the load torque dynamic equations established and their contribution to enhancing control accuracy and improving the operational reliability of the operating mechanism.

Indexed as

Dynamic modelingFeedforward controlHigh voltage vacuum circuit breakerMotor driven operating mechanismTrajectory planning

Identifiers

PMID40379767
PMCPMC12084588

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.