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Record W2248664534 · doi:10.1109/tia.2015.2464307

Apparent Power-Based Anti-Islanding Protection for Distributed Cogeneration Systems

2015· article· en· W2248664534 on OpenAlexaff
S. A. Saleh, A. S. Aljankawey, Ryan Meng, Julian Meng, Liuchen Chang, Chris Diduch

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2015
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsIslandingPower (physics)Computer scienceCogenerationParameterized complexityTopology (electrical circuits)PhysicsElectrical engineeringAlgorithmEngineeringElectric power systemElectricity generationThermodynamics

Abstract

fetched live from OpenAlex

In this paper, the performance of a passive anti-islanding method is experimentally tested for three phase (3φ) cogeneration systems. The tested method is based on determining the wavelet packet transform (WPT) high-frequency subbands present in the d-q-axis components of instantaneous 3qapparent 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 the PCC. An islanding condition can be considered as a transient disturbance that creates nonperiodic and nonstationary high frequency components in sdand sq. These frequency components can be parameterized by WPT high-frequency subbands, which can provide accurate detection of the islanding condition. The d-q WPT-based anti-islanding method is tested for a 3φ cogeneration 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

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)0.0020.001

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 head0.045
GPT teacher head0.255
Teacher spread0.210 · 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

Citations50
Published2015
Admission routes1
Has abstractyes

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