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Record W2022986454 · doi:10.1109/ias.2014.6978506

Instantaneous apparent power-based anti-islanding for distributed co-generation systems

2014· article· en· W2022986454 on OpenAlexaff
S. A. Saleh, A. S. Aljankawey, Ryan Meng, Julian Meng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsIslandingPower (physics)Computer scienceTopology (electrical circuits)Control theory (sociology)PhysicsElectrical engineeringArtificial intelligenceElectric power systemEngineeringControl (management)

Abstract

fetched live from OpenAlex

In this paper, the performance of a passive anti-islanding method is experimentally tested for three phase (3φ) co-generation systems. The tested method is based on determining the wavelet packet transform (WPT) high frequency sub-bands present in the d - q-axis components of instantaneous 3φ apparent powers (sdand sq), when evaluated at the point of common coupling (PCC). This passive anti-islanding method is founded based on the nature of instantaneous 3φ apparent powers that have components continuously exchanged between both sides of PCC. An islanding condition can be considered as a transient disturbance that creates non-periodic and non-stationary high frequency components in sdand sq. These frequency components can be parametrized by WPT high frequency sub-bands, which can provide accurate detection of the islanding condition. The d-q WPT-based anti-islanding method is tested for a 3φ co-generation system under various loading and power delivery conditions. Performance results reveal accurate, fast, and reliable detection and response to the islanding condition.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.227
Teacher spread0.212 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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