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Record W2908378542 · doi:10.1109/naps.2018.8600661

Synchrophasor-Based State Estimation for Voltage Stability Monitoring in Power Systems

2018· article· en· W2908378542 on OpenAlexafffundabout
Xinyun Lu, Xiaozhe Wang, Dmitry Rimorov, Hao Sheng, G. Joós

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsMcGill University
FundersHydro-Québec
KeywordsElectric power systemVoltageComputer scienceVoltage regulationStability (learning theory)State (computer science)Control engineeringPower (physics)EngineeringReliability engineeringReal-time computingAlgorithmElectrical engineering

Abstract

fetched live from OpenAlex

Power distribution systems require continuous monitoring as the integration of renewable energy resources is increasing and the load demand is growing. The implementation of synchrophasors in distribution systems enhances the situational awareness of the system and provides a unique opportunity for developing new monitoring algorithms. This paper proposes a voltage monitoring algorithm based on the synchrophasor-based linear state estimation method. Particularly, the voltage monitoring algorithm combines a set of early warning indicators and the BDS independence test which can detect the voltage instability in a timely manner while avoiding false alarms when the system is still away from the stability boundary. Numerical study has been conducted in the Quebec test feeder to show the effectiveness of the method.

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 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 score0.764
Threshold uncertainty score0.546

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 head0.017
GPT teacher head0.249
Teacher spread0.232 · 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 teacher head, 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

Citations3
Published2018
Admission routes3
Has abstractyes

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