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Application of Magnetic Sensors for Measurement of Current Phasors in Power Systems

2021· article· en· W3216921083 on OpenAlexaff
Prasad Shrawane, T.S. Sidhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCurrent transformerCurrent sensorElectrical engineeringDependabilityElectronic engineeringPhasorSystem of measurementMagnetoresistanceEngineeringReliability (semiconductor)TransformerCurrent (fluid)Computer scienceMagnetic fieldElectric power systemVoltagePower (physics)Reliability engineeringPhysics

Abstract

fetched live from OpenAlex

Digital Instrument transformers play a vital role in digitization of electric power substations and power systems network The advances in magnetic sensors research offer more precise and accurate current measurement that can be useful for monitoring the state of the system and detecting faults and thus leading to more reliability and dependability. Anisotropic magnetoresistive (MR) sensor can be used to measure the AC current by sensing the magnetic field generated by a current carrying conductor. This can be achieved through contactless method which is less complex in installation and maintenance compared to conventional current transformers. This paper describes a novel method of current phasor measurement with the help of a low-cost, broadband and high-sensitivity Tunneling Magnetoresistance (TMR) sensor for noninvasive AC current measurement. A detail analysis of calibration and validation is performed for two sensors and results of comparison of their performances based on a few factors such as distance from current source and insulation are described in this paper.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.248
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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