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A Comprehensive Review of State-of-the-Art Maximum Torque per Ampere Strategies for Permanent Magnet Synchronous Motors

2020· review· en· W3152360869 on OpenAlexaff
Ze Li, Donovan O'Donnell, Wenlong Li, Pengzhao Song, Aiswarya Balamurali, Narayan C. Kar

Bibliographic record

Venuenot available
Typereview
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAmpereTorqueStatorRobustness (evolution)Control theory (sociology)MinificationComputer sciencePropulsionMagnetSynchronous motorPermanent magnet synchronous motorControl engineeringEngineeringCurrent (fluid)Control (management)Artificial intelligencePhysicsMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Permanent magnet synchronous machines (PMSMs) have become highly favored propulsion devices due to their exceptional merits, such as high efficiency and torque density. To operate the motor and drive in an efficient region, maximum torque per ampere control has drawn a great deal of interest among loss minimization strategies. Due to the presence of high nonlinearity and sensitivity to machine parameters, MTPA control strategy requires high robustness and reliability. This paper presents a comprehensive review of the present-day technological developments related to MTPA techniques for PMSM drives. The problems associated with MTPA techniques of PMSM, such as magnetic saturation effect, cross-coupling effect, temperature effect, are systematically summarized to outline the challenges in optimal current angle searching and stator current minimization. Additionally, in terms of machine parameter dependency, the MTPA techniques of PMSM are categorized into machine parameter-dependent and machine parameter independent. Furthermore, the recent advances of MTPA techniques are assessed, and the distinctive features of different methodologies are highlighted.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations14
Published2020
Admission routes1
Has abstractyes

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