Comparative Study of Three Power Management Strategies of a Wind PV Hybrid Stand-alone System for Agricultural Applications
Bibliographic record
Abstract
Desertification due to global warming has a negative impact on the agricultural production capacity of hundreds of thousands of people in the world. To remedy this situation, one of the best ideas is the use of renewable energy sources to pump water and irrigate the fields. The aim of this research is the management of renewable energy hybrid power system (RE-HPS) for agricultural applications. The RE-HPS consists of a Photovoltaic (PV) system and a Wind Turbine (WT). A lead-acid battery bank is used to increase the reliability of the power system. Three Power Management Strategies (PMSs) have been evaluated on their ability to meet the requirements of the pump and loads. The dynamic behavior of the hybrid system was tested under various wind speed, solar radiation, and load demand conditions. The wind speed and solar radiation data are based on real data from Dakar in Senegal. The simulation model was developed using MATLAB/Simulinkl Stateflow. The adopted approach consists in the development of an energy management algorithm capable not only of ensuring the regulation of the water level in the reservoir but also of satisfying the demand for the load and of protecting the batteries from overcharging and deep discharging.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".