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Experience With Hydrogenerator Stator Core Failure, Investigation, and Recommendation

2024· article· en· W4400351866 on OpenAlexaff
Wenli Hong, Muhammad Arshad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsStatorCore (optical fiber)Computer scienceReliability engineeringEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

The failure of the stator core may cause severe damage to hydrogenators, including both the stator winding and core. A number of stator core failures have been occurring on hydro-generators in the last two decades as a result of in-service faults, deterioration, or defects on the stator core, which led to the forced outage of generating units, the reduced service life of the stator, or even the early replacement of entire stators. A series of investigations with deep theoretical calculation and analysis, as well as onsite measurement and testing, were undertaken. However, the stator core failure is complex in nature and attributed to various design, manufacturing, and operating factors, thus making interpreting the stator core condition and preventing future failures challenging. The primary purpose of this paper is to provide typical cases of hydro-generator stator core failures and to share experiences from investigating these failures. The ultimate goal is to increase knowledge about stator core failure modes and actions to prevent catastrophic damage.The requirements for stator core design and testing of new stator cores are recommended. Corrective actions to reduce the stator core degradation or defects are presented as lessons learned from these cases. Suggestions to help utility owners reduce the risk of stator core failures will also be provided.

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 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 score0.287
Threshold uncertainty score0.320

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 communication0.0000.000
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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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