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Record W1998650220 · doi:10.1109/tpwrd.2004.837110

Series compensation of radial power system by a combination of SSSC and dielectric capacitors

2005· article· en· W1998650220 on OpenAlexaff
Fawzi A. Rahman Al Jowder, Boon‐Teck Ooi

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2005
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsMcGill University
Fundersnot available
KeywordsCapacitorReactanceCapacitive sensingElectrical reactanceControl theory (sociology)InductanceCompensation (psychology)InertiaElectric power systemTransient (computer programming)AC powerEngineeringPower (physics)Electrical engineeringComputer sciencePhysicsVoltage

Abstract

fetched live from OpenAlex

This paper shows that the addition of dielectric capacitors lowers the cost of series compensation by static synchronous series compensator (SSSC). Normally the capacitor resonates with the line inductance and the L-C electrical resonance can interact with the torsional resonances of the multiple-inertia elastic shaft of steam turbine-generators to give rise to subsynchronous resonance (SSR) instability. The SSSC has two functions: (1) to provide some series capacitive reactance compensation; (2) to damp out incipient unstable SSR modes. Digital simulation using HYPERSIM shows that for an overall degree of capacitive compensation of 0.7 pu, the SSSC component is only about 1/3 of the capacitive Mvar. Based on the transient stability limit, the transmissibility of the line is increased by a factor of 2.23.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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

Citations65
Published2005
Admission routes1
Has abstractyes

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