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Record W4393180220 · doi:10.1109/ojia.2024.3381856

Noninvasive Measurement of Three-Phase Currents

2024· article· en· W4393180220 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Open Journal of Industry Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPhase (matter)Physics

Abstract

fetched live from OpenAlex

This paper presents a noninvasive method of measuring three-phase currents using magnetic sensors that can be used for continuous monitoring, automation, and protection of power grids. The non-intrusive nature of these sensors gives operational and economic benefits in installing them at the existing distributed generation sites, and power substations. These sensors are linear in operation, free of saturation, and need minimum-duration or no outage for installation as compared to the conventional current transformers. The paper describes magnetic field simulation, calibration, and experimental validation of magnetic sensors for accurate measurement of three-phase currents. Laboratory experiment results of three-phase low current measurements for two types of overhead structures: triangular and horizontal are rendered as a validation of the proposition. The performance verification of these sensors is further achieved by conducting field experiments for measuring currents up to 1500A. The sensors yield promising results with a maximum error of 1.15% in estimation of three-phase currents. The magnetic sensors showed a satisfactory performance in accurately reproducing current waveforms consisting of fundamental frequency and harmonics that are typically present in modern power grids.

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.

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)0.001
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: none
Teacher disagreement score0.677
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.358
Teacher spread0.266 · 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