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Record W2016484454 · doi:10.1109/pesgm.2014.6939190

Optimal location of series FACTS to improve the performance of power system with wind penetration

2014· article· en· W2016484454 on OpenAlexaff
Almoataz Y. Abdelaziz, Mohamed El‐Sharkawy, Mahmoud A. Attia, Ehab F. El‐Saadany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPenetration (warfare)Wind powerElectric power systemControl theory (sociology)Computer scienceAutomotive engineeringEnvironmental scienceEngineeringElectrical engineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

Wind generation connection to power system affects steady state and transient stability. Furthermore, this effect increases with the increase of wind penetration in generation capacity. In this paper, optimal location of series FACTS devices is carried out to solve the steady state problems of wind penetration. Two case studies are carried out on the modified IEEE39 bus system, one with wind reduction to 20% and the second with wind penetration increase by 50% in the two cases system suffers from outage of one generator with load in one of system buses decreased by 15%. The system suffers from minimum voltage reduction, total loss increase and violation of power and power angle limits. This paper found that series FACTS devices in certain range are able to solve these problems associated with wind penetration in power systems.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.004
GPT teacher head0.173
Teacher spread0.169 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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