Performance Investigation of a Large-Scale Grid-Tied PV Plant under High Plateau Climate Conditions: Case Study Ain El-Melh, Algeria
Bibliographic record
Abstract
This paper investigates the performance of a large-scale 20 MW photovoltaic (PV) plant in Ain El-Melh, Algeria.The evaluation is based on experimental data collected from January to December 2019.The PV plant consists of 40 sub-fields with 500 kW inverters and 1936 PV modules of 250 Wp each.Performance parameters including output energy, module temperature, final yield, module/system efficiencies, performance ratio (PR), corrected PR, and other loss-related indicators are assessed.The study focuses on the unique high plateau climate conditions (HPCC) in Algeria and their relevance to largescale grid-tied PV plants.The high plateau climate is characterized by specific environmental factors such as temperature fluctuations, high altitude, and variations in solar irradiance.These factors play a crucial role in determining the performance of largescale grid-tied PV plants in this region.In this study, the CR1000X monitoring device is employed to capture the environment's data, while the NARI SJ-30 monitoring system collects electrical data.During this study, the main environmental factors, such as temperature, radiation, wind speed, precipitation, and relative humidity, which may affect the PV plant's efficiency are considered to assess the PV system's performance.The results show that the PV plant supplied 827.9 MWh to the grid in 2019.The final yield ranged from 3.99 h/day in December to 5.897 h/day in April, and the PR varied from 64.8% to 79.34%.The annual capacity factor ranged from 16.65% to 24.57%.A soiling effect of 4.8% on the performance ratio was observed in a selected subfield.The findings are valuable for researchers, investors, and policymakers involved in PV projects in similar climates, advancing renewable energy utilization.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".