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Record W2936805805 · doi:10.1155/2019/9182761

Decoupling Control Strategy of IPT System Based on Optimal Efficiency Load Tracking

2019· article· en· W2936805805 on OpenAlexvenueno aff
Wenmei Hao, Liwei Zhang, Sanmu Xiu, Fengyu Leng

Bibliographic record

VenueJournal of Advanced Transportation · 2019
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsDecoupling (probability)InductanceControl theory (sociology)Computer scienceVoltageEngineeringControl (management)Control engineeringElectrical engineering

Abstract

fetched live from OpenAlex

The inductive power transmission system is applied to urban rail transit. Due to the limitations of the volume and coupling coefficient of the inductive coupling mechanism and the fact that the fluctuation of air gap in its movement will cause the fluctuation of mutual inductance value, DCDC booster link should be added to the side, rectifying side, to improve the output voltage level and stability. At present, most of the existing control strategies are based on the original side information communication. However, in the application of dynamic wireless charging in urban rail transit, the primary and secondary side coils are in the process of relative movement, so it is relatively difficult to establish reliable real-time communication, and it is easy to be interfered by electromagnetic transmission process, resulting in large errors. This paper analyzes the relationship between load and efficiency of IPT system applied to urban rail transit in detail and obtains the optimal load matching strategy of optimal efficiency. At the same time, an independent control strategy is proposed to eliminate the information communication of the primary and secondary sides and realize decoupling control. Finally, a simulation model is built to verify the effectiveness of the control strategy.

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 score0.250
Threshold uncertainty score0.683

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 head0.005
GPT teacher head0.210
Teacher spread0.204 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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