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Record W4243136092 · doi:10.32920/ryerson.14645928.v1

Design and analysis of transmission-level power system stability control

2021· preprint· en· W4243136092 on OpenAlexaff
Tu Anh Phan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBlackoutElectric power systemControl theory (sociology)Voltage regulatorMATLABStability (learning theory)Power controlGenerator (circuit theory)Power (physics)Control systemEngineeringControl engineeringTransient (computer programming)Computer scienceVoltageControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

After the August 14, 2003 blackout, power system stability probelm has received a great deal of attention. This project is focused on the analysis of transient stability following disturbances. The project investigates three generator-excitation techniques for controlling the stability of power system. They are the manual control (constant excitation voltage), the automatic voltage regulator (AVR), and the automatic voltage regulator plus the power system stabilizer (PSS) that basically can be a led-leg compensator using a rotor speed deviation as an input feedback. This project carried out a computer simulation study of the power system stability for various disturbances conditions. The simulation results from the project have shown that AVR coupled PSS generator-excitation control can achieve the best power system stability, comparing to the manual or AVR control. A major contribution of this project is that a computer platform using MatLab/Simulink software was designed and can be used by other researchers for the power system stability study.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.221
Teacher spread0.195 · 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
Published2021
Admission routes1
Has abstractyes

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Same topicPower System Optimization and StabilityFrench-language works237,207