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Record W2987587914 · doi:10.1109/tpwrd.2019.2895084

A New Filtering Scheme for HVDC Terminals Based on Damped High-Pass Filter

2019· article· en· W2987587914 on OpenAlexafffund
Xin Li, Yang Wang, Wilsun Xu

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2019
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsFilter (signal processing)Electronic engineeringFilter designEngineeringCompensation (psychology)Active filterPrototype filterElectronic filterLow-pass filterHarmonicHigh-pass filterTerminal (telecommunication)Voltage-controlled filterComputer scienceElectrical engineeringVoltageTelecommunicationsAcoustics

Abstract

fetched live from OpenAlex

This paper presents the design and application of a recently developed damped high-pass (DHP) filter for the line-commuted converter HVDC terminals. The DHP filter can damp resonance at the noncharacteristic harmonic frequencies commonly encountered by multipulse HVDC links. A design method is developed to address two unique issues faced by HVDC filter design: a wide range of system impedances seen by a HVDC terminal and a variable reactive compensation configuration at the terminal. Both are challenging filter design problems even for traditional filter banks. Using realistic industry case and sensitivity studies, this paper has shown that the proposed filtering scheme and design method can result in saving in filter costs and space requirements without sacrificing performance in comparison with the common filtering schemes used at HVDC terminals. In addition, the proposed design concepts are also applicable to the design of other filters facing the same design issues.

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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations29
Published2019
Admission routes2
Has abstractyes

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