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Record W2922922810 · doi:10.1109/tpwrd.2019.2906313

An Open Conductor Condition Monitoring Scheme Using Natural Voltage and Current Disturbances

2019· article· en· W2922922810 on OpenAlex
Xi Wang, Tianyu Ding, Wilsun Xu

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2019
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsConductorElectromagnetic coilTransformerVoltageElectrical engineeringElectrical conductorEngineeringElectronic engineeringElectrical equipmentComputer scienceControl theory (sociology)Mathematics

Abstract

fetched live from OpenAlex

Undetected open conductor condition is a threat to the safe operation of power plants and industrial facilities. Open conductor condition is especially difficult to detect in a system with certain Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> connected primary windings, unloaded transformers. This paper presents a novel open conductor condition monitoring scheme for such systems. The scheme utilizes natural voltage disturbances as test signals and detects the open conductor condition by analyzing the responses of the transformer neutral current to these signals. A detection criterion is established based on sequence domain circuit analysis and on considering background voltage unbalances. Implementation issues, including the hardware requirements and the overall monitoring algorithm, are discussed. Simulation and experimental results prove that the proposed scheme can monitor the open conductor condition effectively.

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)0.000
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 score0.292
Threshold uncertainty score0.978

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 communication0.0000.001
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 head0.019
GPT teacher head0.281
Teacher spread0.262 · 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