Evaluation of a Photovoltaic Energy System Applied to a Drip Irrigation System
Bibliographic record
Abstract
This work aimed to evaluate the performance of a drip irrigation system with photovoltaic energy directly connected to the water pumping system through the irrigation uniformity coefficients using the statistical quality control, with the control chart of Shewhart. The experiment was conducted at the State University of West Parana (UNIOESTE), campus of Cascavel, in the Project CASA (Center of Alternative Energy System Analysis), during six days of open sky. The irrigation system contained four lines with 18 drippers each, collecting the flow of each dripper for five minutes, along with the parameters of temperature and solar radiation. In the energy part, the current and the voltage generated by the panel were analyzed. The main results allowed to evaluate that the flows of the drippers showed changes directly proportional to the decrease or increase of the radiation. Even with the changes in radiation levels during the collection period, the system obtained excellent process capability according to the analyzes of the distribution uniformities. It was also concluded that there was a significant relationship between the coefficients (CUC, DUC and CV) with the process capacity of the flow data. The control plot was adequate to diagnose the treatments. Statistical control proved to be effective for the determination of process variability, thus recommending the use of a drip irrigation system with a solar photovoltaic system directly connected to the pump on open sky days with few clouds due to the lower variability in relation to the graph of control and greater uniformity of water distribution.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".