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Record W2046318332 · doi:10.1049/ip-gtd:20045027

Hybrid filters for power quality improvement

2005· article· en· W2046318332 on OpenAlexaff
Bhim Singh, Vishal Verma, Ambrish Chandra, Kamal Al‐Haddad

Bibliographic record

VenueIEE Proceedings - Generation Transmission and Distribution · 2005
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMains electricityPower (physics)Computer scienceQuality (philosophy)State (computer science)Power qualityFilter (signal processing)Electronic engineeringEngineeringReliability engineeringControl engineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Solid-state controllers are widely used to convert AC power for feeding number of electrical loads such as adjustable speed drives, furnaces, power supplies etc. Some of these controllers behave as nonlinear loads because they draw nonsinusoidal current from the AC mains. Filter technology for improving power quality of such loads has matured to a reasonable level. Moreover, hybrid filters are considered one of best options for improving power quality for a number of considerations. A comprehensive review of hybrid filters configurations is given: their control approaches, state of art, design considerations, selection criteria, potential applications, latest trends, future developments and their comparative features. A broad review of the status of hybrid filters to researchers, design and practice engineers dealing with power quality improvements is presented. A classified list of more than 150 research publications on the hybrid filters is also given for quick reference.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.026
GPT teacher head0.264
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 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

Citations234
Published2005
Admission routes1
Has abstractyes

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