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Record W4415169046 · doi:10.1049/icp.2025.1436

Practical solutions to harmonic assessment of inverter-based resource plants

2025· article· en· W4415169046 on OpenAlexaff
Wilsun Xu, Jing Yong

Bibliographic record

VenueIET conference proceedings. · 2025
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHarmonicsHarmonicHarmonic analysisConvertersInterconnectionProbabilistic logicPower (physics)Interface (matter)Total harmonic distortion

Abstract

fetched live from OpenAlex

Inverter-Based Resources (IBRs) are new types of generators entering power systems. There are concerns on the potential harmonic distortions and harmonic resonance impact on the host systems. IBRs use voltage source converter (VSC) to interface with the grid. As a result, IBR units have some unique harmonic generation and response characteristics that are quite differ ent from the traditional line commutated converters (LCC) based loads. This paper presents research findings on several important issues associated with harmonic interconnection studies of IBR plants. Examples include the need for probabilistic summation of the harmonics generated by the IBR units, how to include the impact of a range of grid impedances, and how to quantify the severity of harmonic resonance caused by IBR plants, and so on. The information presented in this paper will help IBR plant designers and power quality engineers to perform harmonic impact assessment properly and with confidence.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.296
Teacher spread0.257 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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