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Record W4304688534 · doi:10.1049/tje2.12197

FPGA‐based control strategy of five‐phase induction motor drives

2022· article· en· W4304688534 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHarmonicsTotal harmonic distortionPower factorField-programmable gate arrayControl theory (sociology)Vector controlCrest factorVoltageInduction motorThree-phaseAC powerHarmonicComputer scienceInverterOvercurrentController (irrigation)Power (physics)EngineeringElectrical engineeringPhysicsComputer hardwareControl (management)Acoustics

Abstract

fetched live from OpenAlex

Abstract Here, a novel control technique for Five‐Phase Induction Motor (FPIM) drives using a field‐programmable gate array (FPGA) controller is proposed. Open‐loop control is analysed in terms of the performance and measurement of various power quality factors, such as voltage harmonics, current harmonics, total harmonic distortion, crest factor, unbalanced, short and long‐term flickering, K‐factor, real power, reactive power, apparent power, and power factor. This study experimentally demonstrated a closed‐loop system with both PID and DTC controls for the FPIM. Diminished torque pulsation is obtained by efficiently utilizing the voltage vectors from the total states of 2 5 = 32. A novel EG voltage vector sequence was proposed for DTC techniques and compared with small, medium, and large voltage vector sequences. Proposed innovative control programming techniques in spartan‐6 XC6SLX25 series FPGA for switching the five‐phase two‐level inverter to reduce the total harmonics distortion by less than 2%.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.330

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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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