Design, Modeling and Optimization of Hybrid Photovoltaic/Wind Turbine System with Battery Storage: Application to Water Pumping
Bibliographic record
Abstract
This paper addresses the modeling, design and optimization of a photovoltaic/wind turbine/batteries system. Tests are made within the area of Bejaia (Algeria), where the solar and wind energies are very exploitable due to its geographical location. The design of the studied system uses the total incident energy approach to determine the size of the photovoltaic and wind generators. The different sources are connected by converters to the DC bus, which optimizes the operation of maximization whatever the weather conditions. The strategy optimization of the PV system is made by three MPPT methods (Pertub & Observ (P&O), Incremental & conductance (INCcond) and fuzzy logic controller (FLC). As for the wind turbine, three MPPT methods have been investigated (Optimal torque control (OTC), Gradient Method (GM) and Fuzzy logic controller (FLC). Following the comparisons of the power, efficiency and response time, the method presenting the best performances is incorporated in the studied system. The simulation is built using Matlab/Simulink. The results obtained during four days of each season (summer, autumn, winter, and spring) are shown and discussed to demonstrate the applicability of the suggested system. To control the water pumping hybrid system, a power control is proposed.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".