MétaCan
Menu
Back to cohort
Record W2122370197 · doi:10.1109/ccece.2006.277404

Wavelet Packet Transform Based Protection of Disturbances in Three-Phase Interior Permanent Magnet Motor Fed from Sinusoidal PWM Voltage Source Inverter

2006· article· en· W2122370197 on OpenAlexaff
M. A. S. K. Khan, T.S. Radwan, M.A. Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPulse-width modulationStatorInverterComputer scienceWaveletFault (geology)Induction motorControl theory (sociology)SIGNAL (programming language)VoltageLine (geometry)Electronic engineeringElectrical engineeringEngineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this work, a novel wavelet packet transform (WPT) based algorithm is developed for the on-line protection of a three-phase interior permanent magnet (IPM) motor fed from a sinusoidal pulse width modulated voltage source inverter (PWM-VSI). The proposed algorithm is implemented and tested on-line using the ds1102 digital signal processor board for diagnosing and detecting different disturbances occurring in the stator terminals of the motor. The criterion for the WPT-based protection is the comparison of the second level high frequency details (dd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of different faulted currents using the selected mother wavelet 'db3' with a threshold determined experimentally during the healthy operating condition of the motor. An experimental setup for the disturbance detection incorporating also the protection circuit is developed in order to accommodate the on-line testings on a laboratory 1-hp IPM motor. In all the tests carried out, the proposed algorithm identified every disturbance promptly and properly, and initiated a trip signal almost at the instant or within one cycle of the fault occurrence based on the 60 Hz systems

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.228
Teacher spread0.220 · 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 designBench or experimental
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

Citations3
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicMachine Fault Diagnosis TechniquesFrench-language works237,207