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Record W2342791491 · doi:10.1109/tpwrd.2015.2507440

Resonance-Free Shunt Capacitors—Configurations, Design Methods and Comparative Analysis

2015· article· en· W2342791491 on OpenAlexafffund
Wilsun Xu, Tianyu Ding, Xin Li, Hao Liang

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2015
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapacitorFilter designPrototype filterElectronic engineeringActive filterFilter capacitorFilter (signal processing)Butterworth filterLow-pass filterVoltage-controlled filterBand-stop filterElectronic filterAdaptive filterm-derived filterElectronic filter topologyControl theory (sociology)Capacitor-input filterComputer scienceEngineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Harmonic resonance has become an important concern for the application of shunt capacitors in recent years. A potential solution to address this challenge is to convert a shunt capacitor into a passive filter. This paper presents design methods to configure a shunt capacitor as a C-type filter or a third-order high-pass filter with guaranteed resonance-free performance. The concept of resonance-free condition is first introduced in this paper. It is then used to develop filter design methods that always meet the resonance-free condition. The two filter configurations are also compared. It was found that the third-order high-pass filter has more advantages than the C-type filter. Another useful finding of this work is that the filter parameters, as determined using the proposed design methods, are independent of the system conditions. As a result, a lookup table for the filter parameters has been created to facilitate immediate use by the industry.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.052
GPT teacher head0.289
Teacher spread0.237 · 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

Citations45
Published2015
Admission routes2
Has abstractyes

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