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Record W919755274 · doi:10.1109/mper.2002.4311892

Methods for Determining the Q-Axis Saturation Characteristics of Salient-Pole Synchronous Machines from the Measured D-Axis Characteristics

2002· article· en· W919755274 on OpenAlex

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.

Bibliographic record

VenueIEEE Power Engineering Review · 2002
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSaturation (graph theory)Reference frameControl theory (sociology)SalientHorizontal axisMathematicsComputer scienceEngineeringFrame (networking)Structural engineering

Abstract

fetched live from OpenAlex

For the accurate analysis of salient-pole synchronous machines using the two-axis frame models, the direct-axis (d-axis) and quadrature-axis (q-axis) saturation characteristics are needed. Usually the d-axis saturation characteristics can be obtained easily by the conventional open-circuit test with the machines excited from their field winding. On the other hand, the q-axis saturation characteristics of synchronous machines cannot be measured applying simple, conventional methods, and, thus, they are usually not available. In this paper, four different methods for calculating the q-axis saturation characteristics of salient-pole synchronous machines from the measured d-axis saturation characteristics are explored. In these methods, the q-axis saturation characteristics can be calculated from the readily available test data, namely the d-axis saturation characteristics, and the d-axis and q-axis unsaturated magnetizing reactances. A comparison between these methods is made.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.260
Teacher spread0.239 · 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