MétaCan
Menu
Back to cohort

Steady State Accuracy of Second-Order Generator Dynamic Models in Generator Parameter Validation Testing

2020· article· en· W3128093941 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

Venue2020 IEEE Electric Power and Energy Conference (EPEC) · 2020
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsGenerator (circuit theory)Computer scienceSteady state (chemistry)Control theory (sociology)Estimation theoryStatorElectric generatorAlgorithmEngineeringPower (physics)Artificial intelligenceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

This article analyzes and compares existing second-order generator dynamic models including two new models under development. Model structures and saturation methods are reviewed for existing popular second-order generator models. Analyses on the steady state accuracy of the dynamic models and their applications in generator model parameter validation testing using stator current interruption method are discussed. Theoretical background of the new GENQEC model is provided and the advantage of the GENQEC in parameter validation is proven through mathematically derived equations from the structure of all the models analyzed.

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 categoriesMeta-epidemiology (narrow)
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 score0.294
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 integrity0.0000.000
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 head0.022
GPT teacher head0.221
Teacher spread0.199 · 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