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Record W4385301952 · doi:10.1139/tcsme-2022-0167

Research on reducing flow shock of the digital hydraulic wind turbine

2023· article· en· W4385301952 on OpenAlexvenueno aff
Zengguang Liu, Benguo Zhang, Liejiang Wei, Daling Yue, Lu Ren, Liqiang Su, Yuyang Zhao

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTurbineEngineeringMarine engineeringHydraulic machineryWind powerAutomotive engineeringMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

The wind turbine with digital hydraulic transmission can make the corresponding hydraulic pump work according to the wind speed, so that the hydraulic wind turbine can maintain high efficiency at the whole working wind speed. However, the switching of different displacement hydraulic pumps will cause a large flow impact on the hydraulic system of the wind turbine, affecting its working characteristics and the absorption of wind energy. Based on the analysis of the working principle of the digital hydraulic wind turbine, the scheme of a 5 MW wind turbine is designed and the AMESim model is established. The dynamic characteristics of the digital hydraulic wind turbine under two switching modes are compared. An advance valve closing control strategy is proposed to reduce the instantaneous flow impact. Simulation results show that the control strategy diminished the system flow shock by about 50% when switching the hydraulic pump. Finally, a hardware-in-loop system with a 5.5 kW digital hydraulic turbine is built to verify the effectiveness of the proposed control strategy. The research results provide a theoretical basis and technical reference for the efficient utilization of wind energy and stable operation of the digital hydraulic wind turbines.

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 score0.090
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.235
Teacher spread0.213 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicWind Turbine Control SystemsFrench-language works237,207