Evidence map›Paper›PMID 36679540›Full record

ArticleSensors (Basel, Switzerland)2023

FPGA-Based Smart Sensor to Detect Current Transformer Saturation during Inrush Current Measurement.

G de J Martínez-Figueroa, Felipe Córcoles-López, Santiago Bogarra

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

3 authors.

G de J Martínez-FigueroaDepartment of Electrical Engineering, ESEIAAT, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain.
Felipe Córcoles-LópezDepartment of Electrical Engineering, ESEIAAT, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain.
Santiago BogarraDepartment of Electrical Engineering, ESEIAAT, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain.ORCID 0000-0002-2006-1156

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current transformer saturation affects measurement accuracy and, consequently, protection reliability. One important concern in the case of overcurrent protections is the discrimination between faults and inrush current in power transformers. This paper presents an FPGA-based smart sensor to detect current transformer saturation, especially during inrush current conditions. Several methods have been proposed in the literature, but some are unsuitable for inrush currents due to their particular waveform. The proposed algorithm implemented on the smart sensor uses two time-domain features of the measured secondary current: the second-order difference function and the third-order statistic central moment. The proposed smart sensor presents high effectiveness and immunity against noise with accurate results in different conditions: different residual flux, resistive burdens, sampling frequency, and noise levels. The points at which saturation starts are detected with an accuracy of approximately 100%. Regarding the end of saturation, the proposed method detects the right ending points with a maximum error of a sample. The smart sensor has been tested on experimental online and real-time conditions (including an anti-aliasing filter) with accurate results. Unlike most existing methods, the proposed smart sensor operates efficiently during inrush conditions. The smart sensor presents high-speed processing despite its simplicity and low computational cost.

Indexed as

AlgorithmsPhysical PhenomenaReproducibility of Resultscurrent transformerFPGAinrush currentsaturationsmart sensor

Identifiers

PMID36679540
PMCPMC9865850

What OpenQuestion holds

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LicenceCC BY
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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.