Evidence map›Paper›PMID 38005457›Full record

ArticleSensors (Basel, Switzerland)2023

Contactless Rotor Ground Fault Detection Method for Brushless Synchronous Machines Based on an AC/DC Rotating Current Sensor.

Miguel A Pardo-Vicente, José M Guerrero, Carlos A Platero, José A Sánchez-Férnandez

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Miguel A Pardo-VicenteHydraulic, Energy and Environmental Engineering Department, Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0002-7542-7645
José M GuerreroElectric Department, Universidad del País Vasco/Euskal Herriko Unibertsitatea, 48013 Bilbao, Spain.ORCID 0000-0003-2096-2031
Carlos A PlateroAutomatic, Electric and Electronic Engineering and Industrial Computing Department, Universidad Politécnica de Madrid, 28003 Madrid, Spain.ORCID 0000-0002-7007-2566
José A Sánchez-FérnandezHydraulic, Energy and Environmental Engineering Department, Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0003-2425-5422

Funding

Universidad Politécnica de Madrid RP2304330031
6 · The paper itself

Abstract

Brushless synchronous machines (BSMs) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes measuring electric parameters in the rotor challenging. It is highly difficult to detect ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSMs is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In the case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor's sensor, inducing voltage in its stator. By analyzing the frequency components of the induced voltage, the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discerns between DC and AC sides. This method does not need any additional power source, slip ring, or brush, which is an important advantage in comparison with the existing methods. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines, achieving satisfactory results.

Indexed as

alternate currentcurrent sensordirect currentrotor fault detection

Identifiers

PMID38005457
PMCPMC10675774

What OpenQuestion holds

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LicenceCC BY
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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.