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Record W2129077469

Coping with multiple Q-V solutions of the WLS state estimator induced by shunt-parameter errors

2004· article· en· W2129077469 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIEEE International Conference on Probabilistic Methods Applied to Power Systems · 2004
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsEstimatorReactanceIterative methodResidualState estimatorAlgorithmMathematicsShunt (medical)VoltageApplied mathematicsControl theory (sociology)Mathematical optimizationComputer scienceStatisticsEngineeringElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

The paper proposes a new iterative algorithm able to cope with multiple Q-V solutions of the WLS state estimator due to shunt-reactance errors. For such errors, it is shown in a 735/230-kV Hydro-Quebec subsystem that the conventional Gauss-Newton iterative algorithm converges to a strongly biased Q-V solution that is not detected as such by the residual statistical tests. By contrast, under no bad measurements, the new iterative algorithm converges to a solution foreseen by the dispatcher via the inclusion of additive state voltage weights in the gain matrix

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)0.000
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: none
Teacher disagreement score0.931
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.319
Teacher spread0.254 · 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