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Online Interturn Short Circuits Fault Monitoring for Permanent Magnet Synchronous Machines

2022· article· en· W4310969376 on OpenAlexaff
Ying Zuo, Ahmad Darabi, Chunyan Lai, K. Lakshmi Varaha Iyer

Bibliographic record

VenueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)StatorFault (geology)Kalman filterExtended Kalman filterReliability (semiconductor)Computer scienceMagnetShort circuitNonlinear systemVoltageElectronic circuitEngineeringControl engineeringPower (physics)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents an unscented Kalman filter (UKF) based method to estimate the severity level of stator interturn short circuit fault in permanent magnet synchronous machines (PMSMs). A mathematical model is firstly built for PMSMs to represent the machine dynamic with inter-turn faults (ISF). The voltage imbalance, inherent asymmetry, and ISF effects are all differentiated analytically by the model. To quantify the fault severity in the presence of unmeasurable fault parameters, the UKF is employed to estimate the nonlinear fault-related quantities in PMSMs, such as the short circuit current, the percentage of shorted turns, and fault loop resistance. The effectiveness and reliability of the proposed method have been validated by extensive simulations.

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 categoriesMeta-epidemiology (narrow)
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 score0.742
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.249
Teacher spread0.216 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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