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Record W2099636429 · doi:10.1109/psce.2009.4839956

A wavelet packet transform-based approach for evaluating the performance of a wind farm subject to voltage sag according to IEEE Standard 1459-2000

2009· article· en· W2099636429 on OpenAlexafffund
Walid G. Morsi, M.E. El-Hawary

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsDalhousie University
FundersKillam Trusts
KeywordsWavelet packet decompositionDiscrete wavelet transformWavelet transformWaveletStationary wavelet transformComputer scienceSecond-generation wavelet transformLifting schemeElectronic engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Traditional definitions of power quantities presented in the IEEE Standard 1459-2000 are compatible only with stationary waveforms, however for non-stationary waveforms, the wavelet-based approach has been shown to be more suitable. This paper develops a new wavelet packet transform (WPT) based approach for measuring the recommended power quantities in the IEEE Standard. The reformulated quantities are then used to evaluate the performance of a wind farm connected to a utility distribution system when subject to voltage sag due to a single-line to ground fault that occurs on a remote bus. The discrete wavelet transform-based approach along with the new wavelet packet transform based approach using Daubechies mother wavelet with order 20 and 43 are used here in order to determine the most appropriate approach and mother wavelet. The results indicate that WPT with db20 can provide the most accurate values for these power quantities that are needed in order to set protection devices, figuring out the correct amount of active power delivered and the reactive power needed to maintain the bus voltage at the specified level.

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 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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.046
GPT teacher head0.302
Teacher spread0.255 · 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 teacher head, 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".

Quick stats

Citations6
Published2009
Admission routes2
Has abstractyes

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