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Record W7091433277 · doi:10.1109/tpel.2025.3622144

Healthy and Open-Phase Fault-Tolerant Unified Control for Dual Three-Phase PMSMs With Rapid Fault Response and False Alarm Recovery Capability

2025· article· en· W7091433277 on OpenAlexaff

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsConcordia University
FundersGuangdong Science and Technology DepartmentNational Natural Science Foundation of China
KeywordsFault (geology)Control theory (sociology)Dual (grammatical number)TorqueALARMFault detection and isolationCopper lossFault toleranceNoise (video)

Abstract

fetched live from OpenAlex

This paper proposes a healthy and open-phase fault tolerant unified control approach for dual three-phase permanent magnet synchronous machines (DT-PMSMs). The proposed approach enables seamless switch between healthy control and fault tolerant control (FTC). When open-phase fault happens, the proposed approach can rapidly respond to the fault within 1/20 electrical cycle. Moreover, when false alarm happens due to noise or disturbance, the proposed approach can switch back to the healthy control effectively. Firstly, a healthy and FT unified control model is derived through the analysis of currents under healthy and fault conditions. The model parameters are optimized to achieve full range minimum copper loss (FRML), and the analytical solutions are derived to improve the computation efficiency and ensure the optimal performance in the full range operating conditions. Compared with existing methods, the proposed approach can achieve higher efficiency and larger torque range. The proposed approach is verified on the test machine under different operating conditions and compared with existing methods in terms of the response speed to fault, copper loss, peak phase current and maximum attainable torque to show the performance improvement.

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: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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 designRandomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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