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Record W1930037519 · doi:10.1109/pesc.2006.1711989

A new hybrid anti-islanding algorithm in grid connected three-phase inverter system

2006· article· en· W1930037519 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
TopicIslanding Detection in Power Systems
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsIslandingInverterAC powerComputer scienceGridControl theory (sociology)Distributed generationPower (physics)Electronic engineeringEngineeringVoltageMathematicsElectrical engineeringControl (management)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

Passive and active methods are two major categories of anti-islanding approaches which are being widely used in grid-connected distributed generation (DG) systems. Passive anti-islanding techniques have no negative impact on the inverter's performance however large non-detection zone is the shortcoming of these techniques. On the other hand, active approaches have smaller non-detection zone, but these active approaches inevitably have negative impact on inverter's performance. To solve these problems, a new hybrid of both passive and active anti-islanding algorithm is proposed in this paper. A covariance index is used as a passive indicator to activate an active anti-islanding action, adaptive reactive power shift action, which can intelligently vary the output reactive power of the DG system to realize the anti-islanding protections. Both simulation and experimental results have demonstrated that this new algorithm can provide a fast anti-islanding protection while assure the zero or the least perturbation in inverter's grid-connected operation.

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 score0.940
Threshold uncertainty score0.935

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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

Citations57
Published2006
Admission routes1
Has abstractyes

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