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Analysis of Open Phase and Phase-to-Phase Short Circuit Fault of PMSM for Electrical Propulsion in an eVTOL

2021· article· en· W3211746720 on OpenAlexafffund
John Ramoul, Gayan Watthewaduge, Alan Dorneles Callegaro, Babak Nahid‐Mobarakeh, Armen Baronian, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
FundersMitacs
KeywordsTorque rippleFault (geology)Control theory (sociology)RippleInverterTorqueThree-phasePropulsionMATLABPhase (matter)Computer scienceEngineeringDirect torque controlElectrical engineeringVoltagePhysicsInduction motorControl (management)Aerospace engineering

Abstract

fetched live from OpenAlex

This paper analyzes permanent magnet synchronous machines (PMSM) under open phase and phase-to-phase fault conditions within an electric vertical take-off and landing (eVTOL) aircraft. The development of a detailed mathematical model for a PMSM under the open phase fault (OPF) and phaseto-phase short circuit fault (P2PSCF) conditions are presented and implemented in MATLAB/Simulink along with its results within a ring bus electrical distribution system (REDS) for an eVTOL is presented. The behavior of both faults is investigated. Fault-tolerant control (FTC) is applied during the post-fault operation, and the copper losses and torque ripple are analyzed. Two fault mitigation techniques, 1) disabling the inverter (FTC1) and 2) creating a virtual neutral point (FTC2) with the inverter, are introduced for the P2PSCF. FTC1 had a peak-to-peak torque ripple of 309Nm with 1.55kW reduction of copper losses. FTC2 was found to respond faster than FTC1 and generated 157Nm peak-to-peak torque ripple. The OPF FTC was analyzed where only current references are changed for the same PI controller structure to enable a fail-operational state for the eVTOL. The OPF FTC achieved 348Nm peak to peak torque ripple compared to 371Nm to pre-fault 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.488

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 head0.055
GPT teacher head0.362
Teacher spread0.307 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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