MétaCan
Menu
Back to cohort
Record W1970682468 · doi:10.1080/15325000802548731

On the Implementation of Time-frequency Transforms for Defining Power Components in Non-sinusoidal Situations: A Survey

2009· article· en· W1970682468 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

VenueElectric Power Components and Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWaveformFrequency domainWavelet transformTime domainWaveletPower (physics)Computer scienceTime–frequency analysisFourier transformQuality (philosophy)Domain (mathematical analysis)Spectral densityElectronic engineeringAlgorithmMathematicsArtificial intelligenceEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Abstract Power quality indices play an important role in decision making in deregulated competitive environments. Useful power quality indices require clear and accepted definitions of power components as well as the RMS values of voltage and current. This is especially true in case of non-stationary distorted waveforms, where neither a frequency-domain–based approach using fast Fourier transform tools nor a time-domain–based approach using real-time data give satisfactory results. Wavelet transform is able to represent any distorted waveform in a time-frequency spectrum while preserving relevant information in both time and frequency domains. Different methods have been proposed in an attempt to define power components in the wavelet domain. This article offers a critical evaluation of the current state-of-the-art concerning this topic. The article also offers conclusions and suggested future work.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.619

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 head0.031
GPT teacher head0.264
Teacher spread0.233 · 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