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Record W3097077432 · doi:10.1109/access.2020.3031693

Modeling of Conducted Emissions for EMI Analysis of Power Converters: State-of-the-Art Review

2020· article· en· W3097077432 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEMIConvertersElectromagnetic interferenceComputer scienceElectronic engineeringNoise (video)Common-mode signalPower (physics)Time domainMode (computer interface)EngineeringElectrical engineeringVoltagePhysicsDigital signal processing

Abstract

fetched live from OpenAlex

Electromagnetic interference issues are associated with high-speed switching of power converters. EMI modeling is an essential tool to study and control the EMI emission, enabling more efficient solutions. A comprehensive review and comparison of different modeling approaches for conducted emissions are provided in this paper, which can be used as a design guideline for engineers. For a motor drive application, common mode and differential mode conducted emissions are studied, and dominant noise production mechanisms are identified. Moreover, a review of various modeling techniques is presented for the main parasitic components of the system. Finally, time domain and frequency domain analysis approaches are explored along with the equivalent circuits which enable fast prediction of EMI emissions. This paper intends to help the reader develop an organized understanding of conducted emission modeling to assist them with a more efficient and electromagnetically-compatible design.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.292
Teacher spread0.253 · 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