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Coordinated Control for High-Power Back-to-Back Inverter Testing with Wide Power Factor and Frequency Range

2024· article· en· W4408281953 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPower factorPower (physics)Power controlInverterElectrical engineeringRange (aeronautics)Control (management)Factor (programming language)Computer scienceEngineeringVoltagePhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Back-to-back inverter testing is commonly used to test high-power inverters in laboratory settings. This test involves a second AC/DC converter to feed the AC power back to the DC bus, thereby reducing the demand on the DC power supply to only compensate for system losses. However, this circulating power path presents challenges in zero-sequence current (ZSC) control, particularly when employing space vector pulse width modulation (SVPWM). This necessitates the implementation of an additional control loop. This paper addresses the aforementioned challenge by proposing a coordinated control strategy between the two converters. A detailed system model is developed, and the proposed method is elaborated upon. Furthermore, the paper analyzes the relationship between power factor, modulation index, and fundamental frequency. In comparison with existing methods, the proposed control strategy enables a wide range of power factor and modulation index, while maintaining the modulation strategy of the inverter under test unchanged, thereby improving testing accuracy. The effectiveness of the proposed method is validated using Matlab/Simulink.

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: none
Teacher disagreement score0.786
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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