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Record W2155990195 · doi:10.1109/tpwrs.2009.2021210

Instantaneous Relaxation-Based Real-Time Transient Stability Simulation

2009· article· en· W2155990195 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransient (computer programming)Real-time simulationComputer scienceStability (learning theory)Electric power systemNonlinear systemTime domainRelaxation (psychology)Power system simulationControl theory (sociology)Set (abstract data type)Control engineeringSimulationPower (physics)EngineeringControl (management)

Abstract

fetched live from OpenAlex

Real-time transient stability simulation is of paramount importance for system security assessment and to initiate preventive control actions before catastrophic events such as blackouts happen. Transient stability simulation of realistic power systems involves the solution of a large set of nonlinear differential-algebraic equations in the time-domain which requires significant computational resources. Exploitation of parallel processing techniques can provide an efficient and cost-effective solution to this problem. This paper proposes a fully parallel method known as instantaneous relaxation (IR) for real-time transient stability simulation. To validate the proposed method, two test systems have been implemented on an advanced PC-cluster-based real-time simulator. A comparison of the captured real-time results with those from the PSS/E software shows high accuracy.

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: none
Teacher disagreement score0.914
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
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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