New fuzzy logic based management strategy to improve hydrogen production from hybrid wind power systems
Bibliographic record
Abstract
This paper presents a new approach to improve the overall efficiency of the Hydrogen Storage System (HSS). Indeed, the amount of hydrogen produced is strongly influenced by the value of the DC-voltage value, since the operating stack voltage increase with electrolyzer's current increasing. Subsequently, increasing of the DC-voltage value allows electrolyzer to absorb much more current, which increasing the performance of the water electrolysis process. Fuzzy logic techniques, which are known as a good tool for nonlinear systems applications such as electrolyzers, are used to regulate the surplus power sent to the electrolyzer where an experimental data are used. Afterwards, a new sizing method of a HSS to increase a hydrogen production, and then increasing the overall efficiency is presented and analyzed. In order to achieve the optimum cost effective of the proposed solution, its performances are analyzed by simulation over one month profiles data. The simulation results which are carried out using Matlab/Simulink environment have highlighted the effectiveness and the increasing the efficiency of HSS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".