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Record W4391661572 · doi:10.1109/tpel.2024.3364087

Selective Harmonic Elimination and Dynamic Enhancement in Magnetic-Characteristic-Free Sensorless Control of SRM Drives at High Speeds

2024· article· en· W4391661572 on OpenAlexaff
Zifeng Chen, Hao Jing, Xinghao Wang, Dehui Luo, Xueqing Wang, Gaoliang Fang, Hang Zhao, Dianxun Xiao

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsControl theory (sociology)Flux linkageSwitched reluctance motorRobustness (evolution)Phase-locked loopComputer scienceRotor (electric)Machine controlMagnetic fluxHarmonic analysisControl engineeringEngineeringControl (management)Electronic engineeringVoltagePhysicsInduction motorMagnetic fieldDirect torque controlPhase noise

Abstract

fetched live from OpenAlex

To address the issues of magnetic parameter dependence and insufficient dynamic performance in high-speed sensorless control of Switched Reluctance Motors (SRMs), this paper proposes a sensorless control scheme based on the Delayed Signal Cancellation Flux Observer (DSCFO) for high-speed SRM drives. This scheme improves the dynamic performance while achieving sensorless control, without the need for magnetic characteristics. Due to the superior filtering efficiency of the DSCFO, the proposed observer eliminates the non-linear terms in the calculated single-phase flux linkage and extracts a pair of orthogonal signals with positional information. Following this, the rotor position and speed information are estimated through the Phase Locked Loop (PLL), enabling sensorless control. Finally, experiments are conducted on a three-phase 5.5 kW 12/8 SRM experimental setup to verify the effectiveness of the proposed scheme. Experimental results indicate that compared to other methods, the proposed approach can achieve sensorless control with only single-phase while possessing desirable accuracy and stronger robustness.

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: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.195
Teacher spread0.192 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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