Development of renewable energy laboratory based on integration of wind, solar and biodiesel energies through a virtual and physical environment
Bibliographic record
Abstract
This paper presents the concept of novel educational renewable energy laboratory. The proposed laboratory objective is to allow students to learn all aspects of renewable energies and take hands-on equipment's for better understanding of clean energy technologies. This laboratory includes a hybrid power system (HPS) integrating wind energy conversion system (WECS), photovoltaic (PV) panel, biodiesel generator, maximum power point tracking (MPPT) controllers and storage battery. In this paper, the whole HPS dimensioning is performed and the three renewable energy source systems are simulated and analyzed separately. The PV panels are examined under solar radiation and cell temperature variation conditions. The solar power functioning are verified and studied through a direct connection to battery and resistive load. The WECS is also analyzed through the same connection under constant wind's speed and variable wind's speed. Furthermore, the biodiesel generator using permanent magnet synchronous machine was simulated and analyzed. The laboratory physical environment is described and the virtual platform is presented. The e-learning concept and the laboratory learning topics are also developed.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".