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Record W2168328418 · doi:10.1109/tpwrd.2003.817726

A practical harmonic resonance guideline for shunt capacitor applications

2003· article· en· W2168328418 on OpenAlexaff
Zhenyu Huang, Wilsun Xu, Venkata Dinavahi

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2003
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCapacitorHarmonicChartHarmonic analysisResonance (particle physics)Power factorElectric power systemVoltageHarmonicsElectronic engineeringShunt (medical)GuidelineEngineeringControl theory (sociology)Electrical engineeringComputer sciencePower (physics)PhysicsAcousticsMathematicsMedicineStatistics

Abstract

fetched live from OpenAlex

Shunt capacitors are extensively used in power systems for voltage support and power factor correction. The proliferation of harmonic-producing loads significantly increases the possibility of system-capacitor resonance. As a result, a practical and easy-to-use procedure to estimate the severity of harmonic resonance is of good interest to industry. The objective of this paper is to present such a method. The paper first proposes a harmonic resonance index. By taking into account the IEEE harmonic limits and the capacitor loading limits, a harmonic resonance chart is developed. A detailed harmonic analysis of the system is needed only if the system condition is located in certain regions of the chart. Examples are given to show how the possibility and severity of harmonic resonance can be estimated using the proposed guideline.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.005

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 head0.037
GPT teacher head0.284
Teacher spread0.246 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations60
Published2003
Admission routes1
Has abstractyes

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