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Record W1992137758 · doi:10.1002/etep.4450120106

Compensation of semi‐periodic currents

2002· article· en· W1992137758 on OpenAlexaff
Leszek S. Czarnecki, G. Chen

Bibliographic record

VenueEuropean Transactions on Electrical Power · 2002
Typearticle
Languageen
FieldEngineering
TopicElectric Power Systems and Control
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsPower electronicsElectronicsCompensation (psychology)Power (physics)Current (fluid)Electrical engineeringVoltagePeriodic functionControl theory (sociology)EngineeringComputer sciencePhysicsControl (management)

Abstract

fetched live from OpenAlex

Abstract Before power electronics were developed, non‐periodic currents used to occur in distribution systems, apart from arc furnaces supply, mainly during switching and faults. Now, such currents are produced at normal operation of some power electronics equipment. Power electronics enables very fast control of processes and energy flow. Non‐periodic currents are by‐products of such a fast control. Non‐periodic currents in electrical power systems occur as an effect of deformation or conversion of periodic, usually sinusoidal voltage and the periodic component of the supply current is crucial for permanent transfer of energy from generators to loads. Such non‐periodic current, with a periodic or quasi‐periodic components are referred to as semi‐periodic currents in this paper. The paper provides fundamentals of semi‐periodic current compensation and discusses a control algorithm of such a compensator.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

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)0.0030.001

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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