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Record W4205971797 · doi:10.1109/tie.2022.3142404

Fault Tolerant Maximum Torque Per Ampere (FT-MTPA) Control for Dual Three-Phase Interior PMSMs Under Open-Phase Fault

2022· article· en· W4205971797 on OpenAlexaff
Guodong Feng, Yuting Lü, Chunyan Lai, Beichen Ding, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of WindsorConcordia University
FundersShenzhen Fundamental Research ProgramNational Natural Science Foundation of China
KeywordsControl theory (sociology)AmpereFault toleranceTorque rippleTorqueStatorFault (geology)Computer scienceDirect torque controlInverterEngineeringCurrent (fluid)Control (management)PhysicsVoltageInduction motorElectrical engineering

Abstract

fetched live from OpenAlex

For the dual three-phase interior permanent magnet synchronous machines (DT-IPMSMs), open-phase fault can cause current unbalance and degrade the drive performance. This article proposes a fault tolerant maximum torque per ampere (FT-MTPA) control for DT-IPMSMs, which can maximize the ratio of the average torque to the stator current and minimize the fault-induced torque ripple. In the proposed approach, optimal FT-MTPA solution is derived and theoretically proven, and current rms is considered as one design constraint to ensure the equivalent loss to the healthy condition. The proposed FT-MTPA control ensures the smooth switching between fault tolerant control and healthy control without inducing noticeable torque ripple. Compared with existing methods, the proposed approach is computation-efficient and fast in achieving the FT-MTPA control, which is critical to practical applications with fast changing loads. Moreover, this article derives the optimal solution from the faulty MTPA model with fault induced terms considered to ensure high torque. Experiments and comparisons with existing methods are conducted to evaluate the proposed approach on a laboratory DT-IPMSM.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.285
Teacher spread0.243 · 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

Citations42
Published2022
Admission routes1
Has abstractyes

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