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Record W156861896

INNOVATIVE POWER SYSTEM TRANSIENT DISTURBANCES DETECTION AND CLASSIFICATION USING WAVELET ANALYSIS

2004· article· en· W156861896 on OpenAlexaff
S. A. Saleh, M.A. Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWaveletMultiresolution analysisPower qualityComputer scienceTransient (computer programming)Electric power systemWavelet transformPattern recognition (psychology)Artificial intelligencePower (physics)Electronic engineeringEngineeringDiscrete wavelet transformVoltageElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

This paper presents an innovative method for detecting and classifying various power system disturbances using wavelet analysis. The proposed method employs a multiresolution analysis (MRA) using localized wavelet basis functions. The nonlinear sub-band time-frequency structure extracted using the MRA can provide the needed features to detect and classify any power system transient disturbance. The proposed method is used in two main applications namely in power transformer protection and in power quality improvement and monitoring applications. The results of applying the proposed method show quick, accurate and effective response to all types of the 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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.243
Teacher spread0.214 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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