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Record W3176626241 · doi:10.1109/tte.2021.3091468

Decoupled Design of Fault-Tolerant Control for Dual-Three-Phase IPMSM With Improved Memory Efficiency and Reduced Current RMS

2021· article· en· W3176626241 on OpenAlexaff
Guodong Feng, Chunyan Lai, Weiwen Peng, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of WindsorConcordia University
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceShenzhen Fundamental Research Program
KeywordsControl theory (sociology)Fault toleranceTorque rippleComputer scienceDual (grammatical number)TorqueTransient (computer programming)Reduction (mathematics)ComputationRippleCurrent (fluid)Fault (geology)HarmonicControl (management)Direct torque controlEngineeringVoltageMathematicsAlgorithmInduction motorElectrical engineering

Abstract

fetched live from OpenAlex

Fault tolerance is critical to real-time control of the dual-three-phase interior permanent magnet synchronous machine (IPMSM). This article proposes an efficient decoupled design for fault-tolerant control (FTC) of dual-three-phase IPMSMs to improve the average torque and minimize the current root mean square (rms) for torque ripple and loss reduction under the open-phase fault. Specifically, the FTC design is divided into two subtasks to derive the FTC strategy with a simplified design, in which the two subtasks are the fundamental current design and the harmonic current design. The optimal current solution to FTC is derived in a memory- and computation-efficient way. The proposed solution can achieve better transient performance and reduce the current rms for FTC, which is critical to practical applications with dynamic changing loads. In comparison with existing methods, the proposed FTC can effectively reduce the request of memory and computation resources from the drive system. Extensive experiments and comparisons are conducted to evaluate the proposed FTC on a laboratory dual-three-phase 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.840

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

Explore more

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