MétaCan
Menu
Back to cohort
Record W1576651827 · doi:10.1109/apec.2015.7104833

A simple approach to current THD prediction for small-scale grid-connected inverters

2015· article· en· W1576651827 on OpenAlexaff
Bo Cao, Liuchen Chang, Riming Shao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsTotal harmonic distortionInverterHarmonicsComputer scienceElectronic engineeringInterconnectionRippleGridPower electronicsControl theory (sociology)Electrical engineeringTopology (electrical circuits)EngineeringVoltageTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

The total harmonic distortion (THD) of the grid current is the key parameter to gauge the performance of power quality for grid-connected inverter output as well as required by the grid interconnection standards, such as AS4777 and IEEE1547. However, the prediction of current THD is complicated and normally accompanied by massive amounts of numerical calculations, which depends on many factors such as control algorithms, modulation techniques, switching frequencies of power electronics devices and etc. In this paper, a simple method that the output current is decomposed into a fundamental component and ripple currents is proposed to estimate the current THD for small-scale grid-connected inverters. In addition, as current harmonics in high frequency ranges can be attenuated or even eliminated by filter technologies, a particular model of current THD performance based on the requirement of the grid interconnection standards is built afterwards. The THD prediction model of the grid current is verified by experiments conducted on a 10kW single-phase inverter.

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: Methods · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.379

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 head0.027
GPT teacher head0.209
Teacher spread0.182 · 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
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

Citations8
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicMicrogrid Control and OptimizationFrench-language works237,207