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Record W2015886925 · doi:10.1109/tie.2014.2336612

New Fault-Resistance Estimation Algorithm for Rotor-Winding Ground-Fault Online Location in Synchronous Machines With Static Excitation

2014· article· en· W2015886925 on OpenAlexfundno aff
F. R. Blánquez, Maria Aranda, E. Rebollo, Francisco Cuadros Blázquez, Carlos A. Platero

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2014
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsnot available
FundersPublic Health Agency of Canada
KeywordsControl theory (sociology)ExcitationElectromagnetic coilEngineeringTransformerVoltagePermanent magnet synchronous generatorRotor (electric)Fault (geology)GroundSynchronous motorField coilElectronic engineeringElectrical engineeringAlgorithmComputer science

Abstract

fetched live from OpenAlex

This paper presents a new algorithm for estimating the ground-fault resistance value in rotor windings. This new algorithm is an improvement of an online ground-fault location method previously presented. This location method is suitable for synchronous generators with static excitation, whose excitation field winding is fed by controlled rectifiers through an excitation transformer. The estimation of the fault resistance is obtained through the comparison between the third-harmonic voltage measured in a grounding resistance placed in the neutral of the excitation transformer and the third-harmonic voltage calculated by the algorithm. This latter variable is obtained with the dc component of the output voltage of the controlled rectifier and the ac supply voltage of this converter. The fault resistance value is used in the novel technique of online ground-fault location, and it allows improving the accuracy of the location of the defect. This new algorithm, integrated in the complete location method, has been tested with satisfactory results in a 5-kVA laboratory synchronous generator and in a 106-MVA hydro-generating unit.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.268
Teacher spread0.256 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations14
Published2014
Admission routes1
Has abstractyes

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