MétaCan
Menu
Back to cohort
Record W2984520160 · doi:10.1109/tte.2019.2953656

Machine Parameter-Independent Maximum Torque Per Ampere Control for Dual Three-Phase PMSMs

2019· article· en· W2984520160 on OpenAlexaff
Ze Li, Guodong Feng, Chunyan Lai, Wenlong Li, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2019
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia UniversityUniversity of Windsor
Fundersnot available
KeywordsControl theory (sociology)AmpereTorqueRobustness (evolution)VoltageMathematicsDual (grammatical number)Computer scienceControl (management)EngineeringPhysics

Abstract

fetched live from OpenAlex

This article proposes a novel dual dc injection-based maximum torque per ampere (MTPA) control algorithm for dual three-phase permanent magnet synchronous machines (PMSMs). The proposed analytical model takes magnetic saturation and temperature effects into account for achieving optimal current angle. In taking consideration of the two effects, a set of small currents are injected in the harmonic plane rather than the fundamental subspace, namely DQ2, which does not interfere with average torque output. In such a way, a novel torque model only involving the command currents and voltages is derived to avoid machine parameters. Furthermore, an MTPA indicator is constructed by the proposed torque to current model to seek the MTPA angle, in which the optimal current angle is found when the indicator magnitude reaches the maximum. In particular, the gradient descent algorithm is employed to ensure the adaptivity and robustness of MTPA control, which offers high real-time capability, low computational cost and low complexity. The experiments validate the proposed MTPA strategy under various load, speed and temperature conditions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.229
Teacher spread0.217 · 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 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

Citations25
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Transportation ElectrificationSame topicMultilevel Inverters and ConvertersFrench-language works237,207