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Record W7055882942

Design and Control of Parallel Three-Phase AC Motor Drives in Battery-Electric Heavy-Haul Freight Locomotives

2024· dissertation· en· W7055882942 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2024
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsController (irrigation)DrivetrainTraction motorStatorTraction power networkDiesel fuelTraction control systemMotor controllerDiesel locomotiveVector control
DOInot available

Abstract

fetched live from OpenAlex

The transportation sector is one of the largest contributors to global greenhouse gas emissions. Due to climate change, many companies operating within the transportation sector are seeking to reduce their emissions, including Ontario Northland Rail (ONR), who operate afreight rail network in northern Ontario. ONR currently relies on diesel powered locomotives for their freight rail operations. To reduce ONR’s greenhouse gas emissions, researchers at the ePOWER facility of Queen’s University have proposed the development of a prototype battery-electric locomotive which could work in tandem with existing diesel locomotives to hybridize a freight train. One important component of any electric vehicle is the motor drive, which is the electronic circuit which manages the flow of power between the electric traction motors and the vehicle’s battery. In this thesis, a parallel motor drive system suitable for high power AC traction applications, such as battery-electric locomotives, is proposed. A novel control scheme is developed which is implemented directly in the natural, abc, reference frame and overcomes the primary challenge when designing parallel motor drive systems: circulating currents. At the core of this control strategy is a proposed Resonant Proportional Integral (RPI) controller, which uses integrated plant dynamics to achieve the functionality of a second-order Proportional Resonant (PR) controller using only a first-order Proportional Integral (PI) controller. Hence the proposed control strategy is very simple, requiring only an inner first-order RPI controller for the stator currents and an outer PI controller for motor speed and maximum torque per ampere (MTPA) operation. A theoretical analysis of the controller is given, which shows the control is robust and stable for all expected motor speeds. The proposed motor drive system is then simulated using the proposed controller and a conventional controller. The proposed control system is found to match the dynamic speed and torque performance of the conventional controller while also effectively suppressing the circulating currents. Prototype inverter modules are then designed and used to experimentally validate the proposed control scheme. The experimental results show that the proposed control method achieves the claimed performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.214
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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