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Record W4403827010 · doi:10.1109/tpwrd.2024.3486566

Modeling of Inverter-Based Resources for Power System Harmonics Studies

2024· article· en· W4403827010 on OpenAlexaff
Roberto Langella, Antonio Bracale, Kuo Lung Lian, Yang Wang, Jason David

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2024
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHarmonicsInverterElectric power systemHarmonic analysisElectronic engineeringPower system harmonicsComputer sciencePower (physics)Grid-tie inverterElectrical engineeringEngineeringVoltageMaximum power point trackingPhysics

Abstract

fetched live from OpenAlex

Inverter-based resources (IBRs) are a new type of generators entering power systems. Industries are concerned about the harmonic impact caused by IBR plants. How to model IBRs for harmonic studies has, therefore, become an important topic. This industry application-oriented tutorial paper presents a comprehensive review and analysis of the harmonic behaviors of IBR units, covering their harmonic characteristics, advanced and practical harmonic models, methods for model parameter determination and more. Lab test results on a physical IBR unit are presented to substantiate the findings. It is hoped this paper will clarify the confusion between the harmonic models of VSC (voltage source converter) based IBR units versus LCC (line commutated converter) based loads. Furthermore, a comparison between IBRs and synchronous generators reveals many similarities in their harmonic behaviors.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.265
Teacher spread0.218 · 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 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

Citations9
Published2024
Admission routes1
Has abstractyes

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