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Record W3159883122 · doi:10.18280/jesa.540212

A Design of Supplementary Controller for UPFC to Improve Damping of Inter-Area Oscillations

2021· article· fr· W3159883122 on OpenAlexvenueno aff
Angshuman Khan, Uttam Narendra Thakur

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2021
Typearticle
Languagefr
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)Unified power flow controllerComputer scienceControl engineeringEngineeringPhysicsControl (management)Electric power systemBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

The conventional highly composite and interrelated power systems sometimes consist of thousands of buses and hundreds of alternators, which need improvement in electric power consumption, consistency maintains, and security.To meet the ever-growing good quality power demand, the improvement of conventional transmission methods and the formation of new concepts that might allow the optimum utilization of accessible power with no reduction of system reliability are of the utmost importance.But due to high complexity, these system responses are oscillatory in nature.If this system oscillation is damped somehow such that the system achieves a new steady operating condition within the transient period, then the system becomes stable.A stable power system requires that the system oscillations should die immediately.This paper proposed a novel control strategy based on a lead-lag based stable controller for a unified power flow controller (UPFC) for faster damping of inter-area oscillations of the system.Each design has been successfully simulated in MATLAB Simulink platform.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.028
GPT teacher head0.266
Teacher spread0.238 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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