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Record W4294931122 · doi:10.3389/fenrg.2022.994629

Adaptive fault-tolerant control of five-phase permanent magnet synchronous motor current using chaotic-particle swarm optimization

2022· article· en· W4294931122 on OpenAlexafffund
Chaobo Chen, Song Ye, Youmin Zhang, Jiaqiang Tian, Song Gao

Bibliographic record

VenueFrontiers in Energy Research · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsControl theory (sociology)Particle swarm optimizationMagnetomotive forceStatorTorque rippleDecoupling (probability)RippleFault (geology)TorqueHarmonicsComputer scienceChaoticDirect torque controlEngineeringPhysicsControl engineeringInduction motorAlgorithmVoltage

Abstract

fetched live from OpenAlex

Both torque ripple and current harmonics are enlarged due to single-phase open-circuit fault of five-phase permanent magnet synchronous motor (FPMSM). Based on chaotic-particle swarm, an adaptive optimization fault tolerant control algorithm is proposed for the FPMSM current. First, Park and Clarke matrices are modified in coordinate transformation process. A reduced-order decoupling matrix is obtained under the open-circuit fault of FPMSM stator winding. Second, the fault-tolerant current is generated with the principle of constant magnetomotive force. Third, the current is adaptively optimized using chaotic-particle swarm algorithm. Hence, motor torque and motor current keep uniform steady state and dynamic performance with them in regular operation. Finally, numerical simulations are carried out to verify the effectiveness of the developed method.

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)0.000
metaresearch head score (Gemma)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 head0.032
GPT teacher head0.290
Teacher spread0.258 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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