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Record W2766919030 · doi:10.1109/tpwrd.2017.2764746

A Shared Resonance Damping Scheme for Multiple Switchable Capacitors

2017· article· en· W2766919030 on OpenAlexafffund
Yang Wang, Wilsun Xu

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsCapacitorScheme (mathematics)Resonance (particle physics)Electronic engineeringMaterials scienceElectrical engineeringOptoelectronicsComputer sciencePhysicsEngineeringVoltageMathematicsAtomic physics

Abstract

fetched live from OpenAlex

An effective measure to mitigate a shunt capacitor caused harmonic resonance is to add a C-type damping unit in series with the capacitor. If there are multiple switchable capacitors, each of them requires a C-type damping unit. The cost and space requirement for implementing these changes, therefore, increase with the number of switchable capacitors. In view of this disadvantage, a scheme that allows multiple switchable capacitors to share a common damping unit is proposed in this paper. The damping unit is designed in such a way that it will provide adequate damping for all possible on/off combinations of the capacitors involved. In addition and proven methodically, the design method can further guarantee resonance free for all possible network impedance conditions. Performance and usefulness of the proposed scheme has been demonstrated through comparative studies on actual capacitor application cases.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.241
Teacher spread0.222 · 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 designBench or experimental
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
Published2017
Admission routes2
Has abstractyes

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