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Establishing Stator Winding Insulation Systems Life Following Industry Standards for Thermal Classification

2023· article· en· W4384344856 on OpenAlexaff
Saeed Ul Haq, Madu TS Moorthy, Bvm Kailash, Thomas Hillmer, Aysha Nayab

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsStatorInsulation systemOriginal equipment manufacturerThermalReliability engineeringElectromagnetic coilThermal insulationMargin (machine learning)EngineeringClass (philosophy)Computer scienceMechanical engineeringAutomotive engineeringElectrical engineeringMaterials scienceComposite materialArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Current VPI and resin rich stator winding insulation systems are widely classified for thermal Class F. To achieve a successful service life, it is expected that a machine should last reliably when operating within specified temperature limits. To achieve a reliable life, it is assumed that maintenance protocols are followed, as per the OEMs instruction manual. The recommended standards to qualify or evaluate insulation systems for a thermal class are IEEE Std. 1776 or IEC Std. 60034-18-31. Typically insulating materials operating at their thermal class limit or temperature will degrade faster. To achieve a significantly longer operational insulation life it is common to maintain margin between the winding temperature rise above ambient and the winding insulation thermal class as specified by NEMA MG-1. In this paper, a comparative study of different industry standards is presented, explaining their differences for establishing a thermal class. In addition, experimental work is presented to show, how thermal classification for a 13.8 kV system is established along with the use of statistical tools.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.280
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes1
Has abstractyes

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