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Record W2006219759 · doi:10.1109/tpwrd.2008.2002660

A New Perspective for the IEEE Standard 1459-2000 Via Stationary Wavelet Transform in the Presence of Nonstationary Power Quality Disturbance

2008· article· en· W2006219759 on OpenAlexaff
Walid G. Morsi, M.E. El-Hawary

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2008
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFast Fourier transformDiscrete wavelet transformStationary wavelet transformHarmonic wavelet transformSecond-generation wavelet transformWavelet transformMathematicsSpectral leakageWaveletS transformDiscrete Fourier transform (general)Prime-factor FFT algorithmAlgorithmElectronic engineeringComputer scienceFourier transformEngineeringShort-time Fourier transformFourier analysisArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

Power components, power factors, and pollution factor are defined according to the IEEE standard 1459-2000 based on the fast Fourier transform (FFT). However, the FFT in the presence of nonstationary power quality (PQ) disturbances results in inaccurate values due to its sensitivity to the spectral leakage problem. In this paper, a new perspective for the IEEE standard 1459-2000 definitions is introduced using the stationary wavelet transform (SWT). As a time-frequency transform, the SWT can provide variable frequency resolution while preserving time information without spectral leakage as the FFT. Moreover, unlike other time-frequency transforms, such as discrete wavelet transform (DWT), SWT possesses the time-invariance property that keeps the time and frequency characteristics throughout all of the decomposition levels. Results of different case studies including stationary, nonstationary of synthetic, and real PQ disturbances proves the effectiveness of applying the SWT over FFT or DWT.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.269
Teacher spread0.241 · 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".

Quick stats

Citations107
Published2008
Admission routes1
Has abstractyes

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