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Record W2017643575 · doi:10.3844/ajassp.2013.570.578

GENERATOR DYNAMIC PERFORMANCE AFFECTED BY PARAMETERâS UNCERTAINTY

2013· article· en· W2017643575 on OpenAlexaff
Khormizi

Bibliographic record

VenueAmerican Journal of Applied Sciences · 2013
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTransient (computer programming)Generator (circuit theory)Control theory (sociology)Electric power systemSensitivity (control systems)Stability (learning theory)Power (physics)Permanent magnet synchronous generatorElectric generatorComputer scienceEngineeringElectronic engineeringVoltageElectrical engineering

Abstract

fetched live from OpenAlex

This study investigates the effect of generator parameters inaccuracy on the transient stability performance of generator and power system. Normally, generator parameters are identified either by calculation based on design specification or by measurements. In both approaches the parameters are obtained with some degree of inaccuracy. Inaccuracy and uncertainty in the obtained parameters can affect the dynamic performance and transient behavior of synchronous generators in which this may affect transient stability evaluation of power systems. In this study by introducing a sensitivity analysis concept for dynamic performance of generator the effect of inaccuracy of generator parameters on the transient stability of power system is evaluated and the acceptable and tolerable inaccuracy in the identification of each parameter is analyzed. The proposed concept and approach is demonstrated on the IEEE 39-bus test.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.199
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2013
Admission routes1
Has abstractyes

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