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Maximum Efficiency Volts-per-Hertz Control of Induction Motor Drives Considering Core Losses, Saturation, and Inverter Losses

2022· article· en· W4295036838 on OpenAlexaff
Yitao Zhang, Sheraz Baig, Taleb Vahabzadeh, Juri Jatskevich

Bibliographic record

Venue2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInverterControl theory (sociology)Induction motorVoltSaturation (graph theory)EngineeringComputer scienceElectrical engineeringVoltageMathematicsControl (management)

Abstract

fetched live from OpenAlex

Inverter-driven induction motors are commonly used in many industrial applications. The conventional Volts-per-Hertz (V/Hz) control, although simple to implement, does not consider core losses, saturation, and inverter losses, thus resulting in poor performance at low speeds and suboptimal operation. However, it is desirable to consider various losses to improve the performance of scalar V/Hz controllers. This paper proposes an improved V/Hz control for inverter-driven induction machines to achieve maximum efficiency by including simplified core losses, saturation, and inverter losses. The losses associated with inverter-driven induction motors are represented in the modified equivalent circuit, whose values are dependent on the operating conditions. Based on analysis of the improved equivalent circuit model, the new maximum efficiency V/Hz ratio can be found for different operating points. The proposed scalar V/Hz control is demonstrated experimentally and by simulations, and it is demonstrated to have an improvement over the conventional scalar V/Hz control method.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.211
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations6
Published2022
Admission routes1
Has abstractyes

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