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Record W2811255638 · doi:10.3390/en11071714

Integrated Full-Frequency Impedance Modeling and Stability Analysis of the Train-Network Power Supply System for High-Speed Railways

2018· article· en· W2811255638 on OpenAlexaff
Xinyu Zhang, Lei Wang, William G. Dunford, Jie Chen, Zhigang Liu

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central UniversitiesBeijing Jiaotong University
KeywordsElectrical impedanceEquivalent impedance transformsAdmittanceControl theory (sociology)Electric power systemTraction (geology)HarmonicEngineeringGridNetwork modelPower (physics)Computer scienceElectronic engineeringElectrical engineeringAcousticsPhysicsControl (management)Mathematics

Abstract

fetched live from OpenAlex

To investigate the harmonic resonance, harmonic instability and low-frequency oscillation phenomena in high-speed railways, this paper proposes a full-frequency impedance model of the train-traction network system (simplified as ‘train-network’) based on d-q coordinates system. Compared with traditional models which deal with only the grid-side converter, the proposed model also includes its load models—the inverter and the traction motor. It also reflects complete control scheme of grid-side converter, which makes it easier to analyze unstable phenomena mentioned above. Moreover, this paper improves the impedance modeling of the network by taking the network impedance and admittance into detailed consideration. In addition, based on the proposed train-network model, the 3D figure and zero-pole diagram are also presented for the analysis of the stability of the integral system. Simulation and experiment results verify the accuracy of the model.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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