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Record W2143784490 · doi:10.1109/naps.2007.4402326

Passive Harmonic Filters for Medium-Voltage Industrial Systems: Practical Considerations and Topology Analysis

2007· article· en· W2143784490 on OpenAlexaff
Alexandre B. Nassif, Wilsun Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHarmonicsHarmonicBand-stop filterTopology (electrical circuits)Total harmonic distortionElectronic engineeringHarmonic analysisElectronic filterFilter (signal processing)Prototype filterSensitivity (control systems)VoltageActive filterEngineeringComputer scienceElectrical engineeringFilter designLow-pass filterAcousticsPhysics

Abstract

fetched live from OpenAlex

Nowadays, passive harmonic filters are the preferred mitigation scheme for harmonics in power systems. Several types of harmonic filters are available, which are installed according to system topology and to the engineer's judgment. The purpose of this paper is to provide comprehensive information about the most common passive filters and to clarify the design and practical considerations. It compares the performance of different filters installed in a medium-voltage industrial plant. It also presents sensitivity studies on different filters and the best detuning frequencies for notch, according to harmonic distortions and components' stresses. It also conducts sensitivity studies on optimizing the detuning for notch, the quality factor, and the number of filter branches considering the cost, components' stresses and harmonic distortions.

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 score0.004
Threshold uncertainty score0.013

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.083
GPT teacher head0.321
Teacher spread0.238 · 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

Citations34
Published2007
Admission routes1
Has abstractyes

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