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Record W2287845755 · doi:10.1109/ecce.2015.7310560

Grid harmonics compensation by using high-power PWM converters based on combination approach

2015· article· en· W2287845755 on OpenAlexaff
Ye Zhang, Yunwei Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHarmonicsPulse-width modulationConvertersElectronic engineeringPWM rectifierHarmonicCompensation (psychology)Computer scienceTotal harmonic distortionHarmonic analysisControl theory (sociology)Power (physics)Active filterEngineeringElectrical engineeringVoltagePhysicsAcoustics

Abstract

fetched live from OpenAlex

For high-power PWM converters, selective harmonic elimination (SHE) modulation scheme is commonly adopted to reduce the low-order harmonics caused by low switching frequency. However, the SHE scheme itself lacks the capability to actively compensate the grid background harmonics. To enable the active compensation ability of the SHE-modulated PWM converters, a selective harmonic compensation (SHC) scheme and an SHE phase jittering method have been proposed in previous works, and their effectiveness to actively attenuate one grid line current harmonic were verified on a high-power PWM current-source rectifier (CSR) system application. Nevertheless, both of the two methods have difficulty in compensating two harmonics simultaneously, which limits their applications with a low resonant frequency of converter system's filter circuit. This paper extends the previous studies to enable the high-power PWM converters to actively compensate two grid background harmonics. The proposed method can not only further reduce the grid line current distortion, but also makes the application of the active compensation no longer limited by the filter circuit. Experimental results of its application on a high-power PWM CSR system are provided to verify the effectiveness.

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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.497

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 head0.015
GPT teacher head0.187
Teacher spread0.173 · 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 teacher head, 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

Citations6
Published2015
Admission routes1
Has abstractyes

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