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Record W2914258653 · doi:10.1109/tpwrd.2019.2894770

A Continuous Monitoring for Neutral Grounding Resistors and Reactors With Hardware Validation

2019· article· en· W2914258653 on OpenAlexafffund
Rahim Jafari, Mital Kanabar, T.S. Sidhu, Ilia Voloh

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2019
Typearticle
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsOntario Tech UniversityWestern University
FundersWestern University
KeywordsGroundResistorTransient (computer programming)Electrical impedanceEngineeringMetering modeVoltageElectronic engineeringLimit (mathematics)Electrical engineeringMATLABSIGNAL (programming language)Earthing systemComputer science

Abstract

fetched live from OpenAlex

Neutral grounding devices (NGDs) are used to limit ground overcurrents, control transient overvoltages, and overcome the consequent issues. Intactness of these apparatuses is vital to prevent the risk of ungrounded or solidly grounded neutral. As such, they should be continuously monitored as targeted here. This paper proposes a continuous monitoring technique that supervises the NGD impedance obtained via full-range voltage and current measurement. It benefits from a new and cost-effective voltage metering mechanism specifically designed for the studied problem. Its performance has been validated using a fabricated prototype. In contrast to existing methods, the application of the proposed technique is not limited to a specific configuration, system operation mode, or NGD. Furthermore, monitoring is performed without signal injection. This monitoring technique shows reliable operation under various conditions of the system and grounding apparatuses based on the analysis performed using PSCAD in conjunction with MATLAB.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes2
Has abstractyes

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Same venueIEEE Transactions on Power DeliverySame topicElectrostatic Discharge in ElectronicsFrench-language works237,207