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Record W4406611234 · doi:10.1109/tia.2025.3531835

Impacts of Impedance Grounding on Variable-Frequency Electric Motor Drives

2025· article· en· W4406611234 on OpenAlexafffund
S. A. Saleh, A. Jee, Julian Meng, E. Ozkop, Sergio Panetta, Daleep Mohla, Babak Nahid‐Mobarakeh

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of New Brunswick
FundersAtlantic Canada Opportunities Agency
KeywordsGroundElectrical impedanceVariable-frequency driveInduction motorVariable (mathematics)AC motorElectric motorElectrical engineeringDirect torque controlSynchronous motorFocused Impedance MeasurementEngineeringControl theory (sociology)Computer scienceAutomotive engineeringPhysicsVoltageControl (management)Power (physics)Mathematics

Abstract

fetched live from OpenAlex

The grounding system for a variable-frequency electric motor drive (VFD) is typically designed to enhance the continuity of operation, limit ground fault currents, limit transient over-voltages during ground faults, improve safety, and reduce or eliminate common-mode voltages (CMVs). The majority of VFDs utilize power transformers to provide isolation between the VFD and its supply, and operate as the point-of-supply to the front-ac-dc power electronic converter (PEC). Grounding system designs for VFDs have been a subject of several standards, industrial codes, and recommended practices. This paper analyzes the performance of impedance grounding systems (during steady-state and fault conditions), when utilized in VFDs. Analyzed grounding systems are the low-resistance, and high-resistance grounding systems. The performance of analyzed grounding systems is evaluated based ground currents, ground potential, and CMVs. Several experimental tests are also conducted to draw conclusions and suggest recommendations for an adequate impedance grounding for industrial VFDs.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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

Citations7
Published2025
Admission routes2
Has abstractyes

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