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Record W2345204546 · doi:10.1109/tpwrs.2015.2496302

Post-Disturbance Transient Stability Status Prediction Using Synchrophasor Measurements

2015· article· en· W2345204546 on OpenAlexaff
Dinesh Rangana Gurusinghe, Athula Rajapakse

Bibliographic record

VenueIEEE Transactions on Power Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTransient (computer programming)Disturbance (geology)Control theory (sociology)Electric power systemVoltageStability (learning theory)EngineeringRotor (electric)Power (physics)Computer sciencePhysicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a novel method to early predict the transient stability status of a power system after being subjected to a severe disturbance. The proposed technique is based on rate of change of voltage vs. voltage deviation (ROCOV-ΔV) characteristics of the post-disturbance voltage magnitudes obtained from synchrophasor measurements. Converging and diverging nature of the post-disturbance trajectories on ROCOV-ΔV plane is used to recognize the transient stability status. The proposed technique is computationally simple and fast compared to the rotor angle based transient stability prediction methods. Offline simulations and real-time experimental studies carried out for the IEEE 39-bus test system showed over 99% overall success rate under symmetrical and asymmetrical faults as well as changes in pre-disturbance conditions and network topology changes.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.243
Teacher spread0.189 · 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

Citations100
Published2015
Admission routes1
Has abstractyes

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