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Record W2078462302 · doi:10.1049/ip-gtd:20000010

Fast computation of post-contingency system margins for voltage stability assessments of large-scale power systems

2000· article· en· W2078462302 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

VenueIEE Proceedings - Generation Transmission and Distribution · 2000
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectric power systemLoad SheddingMargin (machine learning)Control theory (sociology)VoltageStability (learning theory)ContingencyComputationPower (physics)Computer scienceEngineeringAlgorithmElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

A technique to compute the post-contingency system margins for voltage stability assessment is presented. The voltage collapse point of the system base case is first computed using a traditional PV curve method. When a contingency is applied to this fully stressed base case, loads must be shed in order to maintain voltage stability. The key idea of the proposed method is to determine the right amount of load shedding that leaves the post-contingency system at its nose point. The system margin is equal to the base case margin minus the load shedding amount. The amount of load to shed is computed by a modified power flow method, where the amount of loading shedding is parameterised by an additional unknown variable. The technique has been tested on a large-scale power system with 1725 buses. The results show that the proposed approach is very efficient for voltage stability assessment when a large number of contingencies are involved.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.683

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 science0.0000.000
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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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