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Record W2024652979 · doi:10.1109/pedg.2010.5545860

A new islanding detection approach using wavelet packet transform for wind-based distributed generation

2010· article· en· W2024652979 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
KeywordsIslandingWavelet transformComputer scienceWavelet packet decompositionNetwork packetDistributed generationElectric power systemAC powerWaveletElectronic engineeringReal-time computingPower (physics)VoltageEngineeringElectrical engineeringArtificial intelligenceComputer networkPhysics

Abstract

fetched live from OpenAlex

Methods for detecting island in an electric power system are classified as active, passive or hybrid. For certain applications, passive methods are sometimes favored over other detection methods because they are cheap, simple and do not affect the power quality of the electric power system. This paper introduces an islanding detection approach based on wavelet packet transform using only local measurements of voltage and current at the point of common coupling. A new index named node rate of change of power defined in the time-frequency domain is introduced to quantify the rate of change of output power of the distributed generation (DG). The new index has been evaluated in seven different case studies including island case. The presented results have shown that the new index is effectively capable of determining the island case from other types of disturbances.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.730

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 head0.020
GPT teacher head0.227
Teacher spread0.206 · 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

Citations62
Published2010
Admission routes1
Has abstractyes

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