MétaCan
Menu
Back to cohort

Capacitor Banks Switching Transients in Power Systems

2011· article· en· W2123612296 on OpenAlexvenueno aff
Shehab Abdulwadood Ali

Bibliographic record

VenueEnergy science and technology · 2011
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsnot available
Fundersnot available
KeywordsInrush currentTransient (computer programming)OvervoltageCapacitorEmtpInductorResistorReliability (semiconductor)Electrical engineeringElectric power systemEngineeringDecoupling capacitorPower (physics)Computer scienceElectronic engineeringVoltagePhysics

Abstract

fetched live from OpenAlex

This study provides an introduction to capacitor bank switching transients, illustrates the effects of the capacitor banks switching in the utility primary distribution system at different places of the power system, but specially at the customer’s plant. Study covers different operational cases to find the suitable method or techniques can be used to limit the effect of capacitor switching transients. Transient disturbances in power systems may damage key equipment, potentially having a great impact on system reliability. These transients may be introduced during normal switching operations, lightning strikes, or because the equipment failure. Therefore, time-domain computer simulations are developed to study dangers cases due to transient occurrences. The simulations are performed using the simulation software Electromagnetic Transient Program (EMTP). In this study, the Alternative Transients Program (ATP) version 5.7p2 was applied on a simple industry network. Key words : Capacitor banks; Transient overvoltage and current; Energization inrush; Back-to-back switching; Preinsertion resistor and inductor

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.171
Teacher spread0.165 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueEnergy science and technologySame topicPower Systems Fault DetectionFrench-language works237,207